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Baichuan2-13B-Chat_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

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+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+baichuan-inc/Baichuan2-13B-Chat,"---
+language:
+  - en
+  - zh
+license: other
+tasks:
+  - text-generation
+---
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<div align=""center"">
+<h1>
+  Baichuan 2
+</h1>
+</div>
+
+<div align=""center"">
+<a href=""https://github.com/baichuan-inc/Baichuan2"" target=""_blank"">🦉GitHub</a> | <a href=""https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true"" target=""_blank"">💬WeChat</a>
+</div>
+<div align=""center"">
+  百川API支持搜索增强和192K长窗口，新增百川搜索增强知识库、限时免费！<br>
+🚀 <a href=""https://www.baichuan-ai.com/"" target=""_blank"">百川大模型在线对话平台</a> 已正式向公众开放 🎉
+</div>
+
+# 目录/Table of Contents
+
+- [📖 模型介绍/Introduction](#Introduction)
+- [⚙️ 快速开始/Quick Start](#Start)
+- [📊 Benchmark评估/Benchmark Evaluation](#Benchmark)
+- [👥 社区与生态/Community](#Community)
+- [📜 声明与协议/Terms and Conditions](#Terms)
+
+# 更新/Update
+[2023.12.29] 🎉🎉🎉 我们发布了 **[Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) v2** 版本。其中：
+- 大幅提升了模型的综合能力，特别是数学和逻辑推理、复杂指令跟随能力。
+- 使用时需指定revision=v2.0，详细方法参考[快速开始](#Start)
+
+# <span id=""Introduction"">模型介绍/Introduction</span>
+
+Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿**  Tokens 的高质量语料训练，在权威的中文和英文 benchmark
+上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits
+量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表：
+
+Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/). 
+It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. 
+This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. 
+All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:opensource@baichuan-inc.com). 
+The specific release versions and download links are listed in the table below:
+
+|     |          Base Model        |          Chat Model        |       4bits Quantized Chat Model        |
+|:---:|:--------------------:|:--------------------:|:--------------------------:|
+| 7B  | [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base)  | [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)  | [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits)  |
+| 13B | [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) |
+
+# <span id=""Start"">快速开始/Quick Start</span>
+
+在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。
+
+In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment.
+
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.utils import GenerationConfig
+tokenizer = AutoTokenizer.from_pretrained(""baichuan-inc/Baichuan2-13B-Chat"",
+    revision=""v2.0"",
+    use_fast=False,
+    trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(""baichuan-inc/Baichuan2-13B-Chat"",
+    revision=""v2.0"",
+    device_map=""auto"",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True)
+model.generation_config = GenerationConfig.from_pretrained(""baichuan-inc/Baichuan2-13B-Chat"", revision=""v2.0"")
+messages = []
+messages.append({""role"": ""user"", ""content"": ""解释一下“温故而知新”""})
+response = model.chat(tokenizer, messages)
+print(response)
+""温故而知新""是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。
+
+这句话鼓��我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。
+```
+**注意：如需使用老版本，需手动指定revision参数，设置revision=v1.0** 
+
+# <span id=""Benchmark"">Benchmark 结果/Benchmark Evaluation</span>
+
+我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。
+
+We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md).
+
+### 7B Model Results
+
+|                         | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                         |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|        **GPT-4**        | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|    **GPT-3.5 Turbo**    | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|      **LLaMA-7B**       | 27.10      | 35.10    | 26.75     | 27.81      | 28.17       | 32.38   |
+|      **LLaMA2-7B**      | 28.90      | 45.73    | 31.38     | 25.97      | 26.53       | 39.16   |
+|       **MPT-7B**        | 27.15      | 27.93    | 26.00     | 26.54      | 24.83       | 35.20   |
+|      **Falcon-7B**      | 24.23      | 26.03    | 25.66     | 24.24      | 24.10       | 28.77   |
+|     **ChatGLM2-6B**     | 50.20      | 45.90    | 49.00     | 49.44      | 45.28       | 31.65   |
+|    **[Baichuan-7B]**    | 42.80      | 42.30    | 44.02     | 36.34      | 34.44       | 32.48   |
+| **[Baichuan2-7B-Base]** | 54.00      | 54.16    | 57.07     | 47.47      | 42.73       | 41.56   |
+
+### 13B Model Results
+
+|                             | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                             |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|          **GPT-4**          | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|      **GPT-3.5 Turbo**      | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|        **LLaMA-13B**        | 28.50      | 46.30    | 31.15     | 28.23      | 28.22       | 37.89   |
+|       **LLaMA2-13B**        | 35.80      | 55.09    | 37.99     | 30.83      | 32.29       | 46.98   |
+|       **Vicuna-13B**        | 32.80      | 52.00    | 36.28     | 30.11      | 31.55       | 43.04   |
+| **Chinese-Alpaca-Plus-13B** | 38.80      | 43.90    | 33.43     | 34.78      | 35.46       | 28.94   |
+|       **XVERSE-13B**        | 53.70      | 55.21    | 58.44     | 44.69      | 42.54       | 38.06   |
+|   **[Baichuan-13B-Base]**    | 52.40      | 51.60    | 55.30     | 49.69      | 43.20       | 43.01   |
+|  **[Baichuan2-13B-Base]**   | 58.10      | 59.17    | 61.97     | 54.33      | 48.17       | 48.78   |
+
+## 训练过程模型/Training Dynamics
+
+除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用
+（[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化：
+
+In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU.
+
+![checkpoint](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/checkpoints.jpeg)
+
+# <span id=""Community"">社区与生态/Community</span>
+
+## Intel 酷睿 Ultra 平台运行百川大模型
+
+使用酷睿™/至强® 可扩展处理器或配合锐炫™ GPU等进行部署[Baichuan2-7B-Chat]，[Baichuan2-13B-Chat]模型，推荐使用 BigDL-LLM([CPU], [GPU])以发挥更好推理性能。
+
+详细支持信息可参考[中文操作手册](https://github.com/intel-analytics/bigdl-llm-tutorial/tree/main/Chinese_Version)，包括用notebook支持，[加载，优化，保存方法](https://github.com/intel-analytics/bigdl-llm-tutorial/blob/main/Chinese_Version/ch_3_AppDev_Basic/3_BasicApp.ipynb)等。
+
+When deploy on Core™/Xeon® Scalable Processors or with Arc™ GPU, BigDL-LLM ([CPU], [GPU]) is recommended to take full advantage of better inference performance.
+
+# <span id=""Terms"">声明与协议/Terms and Conditions</span>
+
+## 声明
+
+我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用
+Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2
+模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
+
+我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用
+Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
+
+We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment.
+
+We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility.
+
+## 协议
+
+社区使用 Baichuan 2 模型需要遵循 [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) 和[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)。Baichuan 2 模型支持商业用途，如果您计划将 Baichuan 2 模型或其衍生品用于商业目的，请您确认您的主体符合以下情况：
+  1. 您或您的关联方的服务或产品的日均用户活跃量（DAU）低于100万。
+  2. 您或您的关联方不是软件服务提供商、云服务提供商。
+  3. 您或您的关联方不存在将授予您的商用许可，未经百川许可二次授权给其他第三方的可能。
+
+在符合以上条件的前提下，您需要通过以下联系邮箱 opensource@baichuan-inc.com ，提交《Baichuan 2 模型社区许可协议》要求的申请材料。审核通过后，百川将特此授予您一个非排他性、全球性、不可转让、不可再许可、可撤销的商用版权许可。
+
+The community usage of Baichuan 2 model requires adherence to [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) and [Community License for Baichuan2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). The Baichuan 2 model supports commercial use. If you plan to use the Baichuan 2 model or its derivatives for commercial purposes, please ensure that your entity meets the following conditions:
+
+  1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
+  2. Neither you nor your affiliates are software service providers or cloud service providers.
+  3. There is no possibility for you or your affiliates to grant the commercial license given to you, to reauthorize it to other third parties without Baichuan's permission.
+
+Upon meeting the above conditions, you need to submit the application materials required by the Baichuan 2 Model Community License Agreement via the following contact email: opensource@baichuan-inc.com. Once approved, Baichuan will hereby grant you a non-exclusive, global, non-transferable, non-sublicensable, revocable commercial copyright license.
+
+
+[GitHub]:https://github.com/baichuan-inc/Baichuan2
+[Baichuan2]:https://github.com/baichuan-inc/Baichuan2
+
+[Baichuan-7B]:https://huggingface.co/baichuan-inc/Baichuan-7B
+[Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base
+[Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
+[Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits
+[Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base
+[Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base
+[Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
+[Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits
+
+[通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F
+[法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91
+
+[《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf
+
+[邮件申请]: mailto:opensource@baichuan-inc.com
+[Email]: mailto:opensource@baichuan-inc.com
+[opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com
+[训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints
+[百川智能]: https://www.baichuan-ai.com
+
+[CPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2
+[GPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2","{""id"": ""baichuan-inc/Baichuan2-13B-Chat"", ""author"": ""baichuan-inc"", ""sha"": ""c8d877c7ca596d9aeff429d43bff06e288684f45"", ""last_modified"": ""2024-02-26 08:58:32+00:00"", ""created_at"": ""2023-08-29 02:30:01+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 7032, ""downloads_all_time"": null, ""likes"": 424, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""baichuan"", ""text-generation"", ""custom_code"", ""en"", ""zh"", ""license:other"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\n- zh\nlicense: other\ntasks:\n- text-generation"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""BaichuanForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_baichuan.BaichuanConfig"", ""AutoModelForCausalLM"": ""modeling_baichuan.BaichuanForCausalLM""}, ""model_type"": ""baichuan"", ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<s>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""</s>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}, ""pad_token"": {""__type"": ""AddedToken"", ""content"": ""<unk>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}, ""unk_token"": {""__type"": ""AddedToken"", ""content"": 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EmbeddedLLM/chat-template-generation, IS2Lab/S-Eval, Justinrune/LLaMA-Factory, Zulelee/langchain-chatchat, blackwingedkite/gutalk, eduagarcia/open_pt_llm_leaderboard, evelyn-lo/evelyn, huggingface/InferenceSupport/discussions/new?title=baichuan-inc/Baichuan2-13B-Chat&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bbaichuan-inc%2FBaichuan2-13B-Chat%5D(%2Fbaichuan-inc%2FBaichuan2-13B-Chat)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kenken999/fastapi_django_main_live, martinakaduc/melt, xu-song/kplug",13
+zimyu/baichuan2-13b-zsee-lora,"---
+base_model:
+- baichuan-inc/Baichuan2-13B-Chat
+tags:
+- chemistry
+---
+This LoRA model was fine-tuned using the zeolite synthesis dataset ZSEE.
+
+Usage:
+```
+import torch
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    GenerationConfig
+)
+from peft import PeftModel
+device = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")
+model_path = 'baichuan-inc/Baichuan2-13B-Chat'
+lora_path = 'zimyu/baichuan2-13b-zsee-lora'
+
+config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    config=config,
+    device_map=""auto"",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(
+    model,
+    lora_path,
+)
+model.eval()
+
+system_prompt = ""<<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n""
+sintruct = ""{\""instruction\"": \""You are an expert in event argument extraction. Please extract event arguments and their roles from the input that conform to the schema definition, which already includes event trigger words. If an argument does not exist, return NAN or an empty dictionary. Please respond in the format of a JSON string.\"", \""schema\"": [{\""event_type\"": \""Add\"", \""trigger\"": [\""added\""], \""arguments\"": [\""container\"", \""material\"", \""temperature\""]}, {\""event_type\"": \""Stir\"", \""trigger\"": [\""stirred\""], \""arguments\"": [\""sample\"", \""revolution\"", \""temperature\"", \""duration\""]}], \""input\"": \""Subsequently , the pre-prepared silicalite-1 seed was added to the above mixture and stirred for another 1 h , and the quantity of seed equals to 7.0 wt% of the total SiO2 in the starting gel .\""}""
+sintruct = '[INST] ' + system_prompt + sintruct + ' [/INST]'
+
+input_ids = tokenizer.encode(sintruct, return_tensors=""pt"").to(device)
+input_length = input_ids.size(1)
+generation_output = model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_length=512, max_new_tokens=256, return_dict_in_generate=True))
+generation_output = generation_output.sequences[0]
+generation_output = generation_output[input_length:]
+output = tokenizer.decode(generation_output, skip_special_tokens=True)
+
+print(output)
+```
+
+Output:
+```
+{""Add"": [{""container"": ""NAN"", ""material"": [""above mixture"", ""pre-prepared silicalite-1 seed""], ""temperature"": ""NAN""}], ""Stir"": [{""sample"": ""NAN"", ""revolution"": ""NAN"", ""temperature"": ""NAN"", ""duration"": ""1 h""}]}
+```","{""id"": ""zimyu/baichuan2-13b-zsee-lora"", ""author"": ""zimyu"", ""sha"": ""4cbdd08c1d7b96d8ee9183f594187e54541537bb"", ""last_modified"": ""2025-01-08 13:15:57+00:00"", ""created_at"": ""2025-01-08 03:56:17+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""safetensors"", ""chemistry"", ""base_model:baichuan-inc/Baichuan2-13B-Chat"", ""base_model:finetune:baichuan-inc/Baichuan2-13B-Chat"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- baichuan-inc/Baichuan2-13B-Chat\ntags:\n- chemistry"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-01-08 13:15:57+00:00"", ""cardData"": ""base_model:\n- baichuan-inc/Baichuan2-13B-Chat\ntags:\n- chemistry"", ""transformersInfo"": null, ""_id"": ""677df7617b04df2925cafa2f"", ""modelId"": ""zimyu/baichuan2-13b-zsee-lora"", ""usedStorage"": 892654240}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=zimyu/baichuan2-13b-zsee-lora&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bzimyu%2Fbaichuan2-13b-zsee-lora%5D(%2Fzimyu%2Fbaichuan2-13b-zsee-lora)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Baichuan2-7B-Chat_finetunes_20250427_003734.csv_finetunes_20250427_003734.csv

@@ -0,0 +1,235 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+baichuan-inc/Baichuan2-7B-Chat,"---
+language:
+  - en
+  - zh
+license_name: baichuan2-community-license
+license_link: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/main/Community%20License%20for%20Baichuan2%20Model.pdf
+tasks:
+  - text-generation
+---
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<div align=""center"">
+<h1>
+  Baichuan 2
+</h1>
+</div>
+
+<div align=""center"">
+<a href=""https://github.com/baichuan-inc/Baichuan2"" target=""_blank"">🦉GitHub</a> | <a href=""https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true"" target=""_blank"">💬WeChat</a>
+</div>
+<div align=""center"">
+  百川API支持搜索增强和192K长窗口，新增百川搜索增强知识库、限时免费！<br>
+🚀 <a href=""https://www.baichuan-ai.com/"" target=""_blank"">百川大模型在线对话平台</a> 已正式向公众开放 🎉
+</div>
+
+# 目录/Table of Contents
+
+- [📖 模型介绍/Introduction](#Introduction)
+- [⚙️ 快速开始/Quick Start](#Start)
+- [📊 Benchmark评估/Benchmark Evaluation](#Benchmark)
+- [👥 社区与生态/Community](#Community)
+- [📜 声明与协议/Terms and Conditions](#Terms)
+
+
+# <span id=""Introduction"">模型介绍/Introduction</span>
+
+Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿**  Tokens 的高质量语料训练，在权威的中文和英文 benchmark
+上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits
+量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表：
+
+Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/). 
+It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. 
+This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. 
+All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:opensource@baichuan-inc.com). 
+The specific release versions and download links are listed in the table below:
+
+|     |          Base Model        |          Chat Model        |       4bits Quantized Chat Model        |
+|:---:|:--------------------:|:--------------------:|:--------------------------:|
+| 7B  | [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base)  | [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)  | [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits)  |
+| 13B | [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) |
+
+# <span id=""Start"">快速开始/Quick Start</span>
+
+在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。
+
+In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment.
+
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.utils import GenerationConfig
+tokenizer = AutoTokenizer.from_pretrained(""baichuan-inc/Baichuan2-7B-Chat"", use_fast=False, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(""baichuan-inc/Baichuan2-7B-Chat"", device_map=""auto"", torch_dtype=torch.bfloat16, trust_remote_code=True)
+model.generation_config = GenerationConfig.from_pretrained(""baichuan-inc/Baichuan2-7B-Chat"")
+messages = []
+messages.append({""role"": ""user"", ""content"": ""解释一下“温故而知新”""})
+response = model.chat(tokenizer, messages)
+print(response)
+""温故而知新""是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。
+
+这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。
+```
+
+# <span id=""Benchmark"">Benchmark ��果/Benchmark Evaluation</span>
+
+我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。
+
+We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md).
+
+### 7B Model Results
+
+|                         | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                         |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|        **GPT-4**        | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|    **GPT-3.5 Turbo**    | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|      **LLaMA-7B**       | 27.10      | 35.10    | 26.75     | 27.81      | 28.17       | 32.38   |
+|      **LLaMA2-7B**      | 28.90      | 45.73    | 31.38     | 25.97      | 26.53       | 39.16   |
+|       **MPT-7B**        | 27.15      | 27.93    | 26.00     | 26.54      | 24.83       | 35.20   |
+|      **Falcon-7B**      | 24.23      | 26.03    | 25.66     | 24.24      | 24.10       | 28.77   |
+|     **ChatGLM2-6B**     | 50.20      | 45.90    | 49.00     | 49.44      | 45.28       | 31.65   |
+|    **[Baichuan-7B]**    | 42.80      | 42.30    | 44.02     | 36.34      | 34.44       | 32.48   |
+| **[Baichuan2-7B-Base]** | 54.00      | 54.16    | 57.07     | 47.47      | 42.73       | 41.56   |
+
+### 13B Model Results
+
+|                             | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
+|:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
+|                             |  5-shot    |  5-shot  |  5-shot   | 5-shot     | 5-shot      | 3-shot  |
+|          **GPT-4**          | 68.40      | 83.93    | 70.33     | 66.15      | 63.27       | 75.12   |
+|      **GPT-3.5 Turbo**      | 51.10      | 68.54    | 54.06     | 47.07      | 46.13       | 61.59   |
+|        **LLaMA-13B**        | 28.50      | 46.30    | 31.15     | 28.23      | 28.22       | 37.89   |
+|       **LLaMA2-13B**        | 35.80      | 55.09    | 37.99     | 30.83      | 32.29       | 46.98   |
+|       **Vicuna-13B**        | 32.80      | 52.00    | 36.28     | 30.11      | 31.55       | 43.04   |
+| **Chinese-Alpaca-Plus-13B** | 38.80      | 43.90    | 33.43     | 34.78      | 35.46       | 28.94   |
+|       **XVERSE-13B**        | 53.70      | 55.21    | 58.44     | 44.69      | 42.54       | 38.06   |
+|   **[Baichuan-13B-Base]**    | 52.40      | 51.60    | 55.30     | 49.69      | 43.20       | 43.01   |
+|  **[Baichuan2-13B-Base]**   | 58.10      | 59.17    | 61.97     | 54.33      | 48.17       | 48.78   |
+
+## 训练过程模型/Training Dynamics
+
+除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用
+（[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化：
+
+In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU.
+
+![checkpoint](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/checkpoints.jpeg)
+
+# <span id=""Community"">社区与生态/Community</span>
+
+## Intel 酷睿 Ultra 平台运行百川大模型
+
+使用酷睿™/至强® 可扩展处理器或配合锐炫™ GPU等进行部署[Baichuan2-7B-Chat]，[Baichuan2-13B-Chat]模型，推荐使用 BigDL-LLM([CPU], [GPU])以发挥更好推理性能。
+
+详细支持信息可参考[中文操作手册](https://github.com/intel-analytics/bigdl-llm-tutorial/tree/main/Chinese_Version)，包括用notebook支持，[加载，优化，保存方法](https://github.com/intel-analytics/bigdl-llm-tutorial/blob/main/Chinese_Version/ch_3_AppDev_Basic/3_BasicApp.ipynb)等。
+
+When deploy on Core™/Xeon® Scalable Processors or with Arc™ GPU, BigDL-LLM ([CPU], [GPU]) is recommended to take full advantage of better inference performance.
+
+# <span id=""Terms"">声明与协议/Terms and Conditions</span>
+
+## 声明
+
+我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用
+Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2
+模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
+
+我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用
+Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
+
+We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment.
+
+We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility.
+
+## 协议
+
+社区使用 Baichuan 2 模型需要遵循 [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) 和[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)。Baichuan 2 模型支持商业用途，如果您计划将 Baichuan 2 模型或其衍生品用于商业目的，请您确认您的主体符合以下情况：
+  1. 您或您的关联方的服务或产品的日均用户活跃量（DAU）低于100万。
+  2. 您或您的关联方不是软件服务提供商、云服务提供商。
+  3. 您或您的关联方不存在将授予您的商用许可，未经百川许可二次授权给其他第三方的可能。
+
+在符合以上条件的前提下，您需要通过以下联系邮箱 opensource@baichuan-inc.com ，提交《Baichuan 2 模型社区许可协议》要求的申请材料。审核通过后，百川将特此授予您一个非排他性、全球性、不可转让、不可再许可、可撤销的商用版权许可。
+
+The community usage of Baichuan 2 model requires adherence to [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) and [Community License for Baichuan2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). The Baichuan 2 model supports commercial use. If you plan to use the Baichuan 2 model or its derivatives for commercial purposes, please ensure that your entity meets the following conditions:
+
+  1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
+  2. Neither you nor your affiliates are software service providers or cloud service providers.
+  3. There is no possibility for you or your affiliates to grant the commercial license given to you, to reauthorize it to other third parties without Baichuan's permission.
+
+Upon meeting the above conditions, you need to submit the application materials required by the Baichuan 2 Model Community License Agreement via the following contact email: opensource@baichuan-inc.com. Once approved, Baichuan will hereby grant you a non-exclusive, global, non-transferable, non-sublicensable, revocable commercial copyright license.
+
+
+[GitHub]:https://github.com/baichuan-inc/Baichuan2
+[Baichuan2]:https://github.com/baichuan-inc/Baichuan2
+
+[Baichuan-7B]:https://huggingface.co/baichuan-inc/Baichuan-7B
+[Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base
+[Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
+[Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits
+[Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base
+[Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base
+[Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
+[Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits
+
+[通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F
+[法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
+[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
+[多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91
+
+[《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf
+
+[邮件申请]: mailto:opensource@baichuan-inc.com
+[Email]: mailto:opensource@baichuan-inc.com
+[opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com
+[训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints
+[百川智能]: https://www.baichuan-ai.com
+
+[CPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2
+[GPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2
+","{""id"": ""baichuan-inc/Baichuan2-7B-Chat"", ""author"": ""baichuan-inc"", ""sha"": ""ea66ced17780ca3db39bc9f8aa601d8463db3da5"", ""last_modified"": ""2024-02-26 08:58:12+00:00"", ""created_at"": ""2023-08-29 02:21:41+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 13874, ""downloads_all_time"": null, ""likes"": 165, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""baichuan"", ""text-generation"", ""custom_code"", ""en"", ""zh"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\n- zh\nlicense_name: baichuan2-community-license\nlicense_link: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/main/Community%20License%20for%20Baichuan2%20Model.pdf\ntasks:\n- text-generation"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""BaichuanForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_baichuan.BaichuanConfig"", ""AutoModelForCausalLM"": ""modeling_baichuan.BaichuanForCausalLM""}, ""model_type"": ""baichuan"", ""tokenizer_config"": {""eos_token"": {""__type"": ""AddedToken"", ""content"": ""</s>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}, ""pad_token"": {""__type"": ""AddedToken"", ""content"": ""<unk>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}, ""unk_token"": {""__type"": ""AddedToken"", ""content"": ""<unk>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": true}}}, ""transformers_info"": 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""stevending1st/myLagent"", ""z0312z/Lagent"", ""ilemon/Internlm2.5LLaMAindexRAG"", ""shaoshaoshao/agent_by_lagent"", ""leledaidai/Lagent_practice"", ""msun415/Llamole"", ""shuyaya-moon/Lagent"", ""sanjion9/Lagent-Agent"", ""seachen/Lagent-L2G2000-ymh"", ""magic2025/Lagent-Malone"", ""ddddpao/Lagent"", ""kazenokizi/andrew-s-lagent"", ""D4isyC/Lagent-Demo"", ""Penkris/agent"", ""Rtwotwo/Lagent"", ""Testdevk-1/Lagent_Multi-Agent"", ""leishenggungun/Lagent"", ""deswa/Lagent"", ""SutaLXY/internlm_lagent"", ""FMY714/internlm2.5_multiagent"", ""tianleiSHI/Multi-Agent"", ""Taoismimmortal/LagentAPPwithDouble"", ""HuHu1226/L2G2000-Lagent"", ""jiahm/Lagent_Learn"", ""sakuralggm/lagent"", ""kingwe/agents_Lagent"", ""CCCasEEE/internlm_lagent"", ""luckDW1126/Lagent"", ""Penkris/lagent111"", ""wowwang666/test-Multi-Agents1"", ""tjorange/lagent"", ""pipi333/Lingxi_Weather_Sprite""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-02-26 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https://huggingface.co/tjluyao/baichuan2-7b-chat-lora2",6,"https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q8_0-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q6_K-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q5_K_M-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q5_K_S-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q5_0-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q4_K_M-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q4_K_S-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q4_0-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q3_K_L-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q3_K_M-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q3_K_S-GGUF, https://huggingface.co/CHE-72/Baichuan2-7B-Chat-Q2_K-GGUF, https://huggingface.co/Junrui2021/Baichuan2-7B-Chat-Q4_K_M-GGUF, https://huggingface.co/mradermacher/Baichuan2-7B-Chat-GGUF, https://huggingface.co/mradermacher/Baichuan2-7B-Chat-i1-GGUF",16,,0,"Jianfei217/MultiAgents, Justinrune/LLaMA-Factory, Penkris/agent, Yiyuan/VSA, Zulelee/langchain-chatchat, blackwingedkite/gutalk, huggingface/InferenceSupport/discussions/new?title=baichuan-inc/Baichuan2-7B-Chat&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bbaichuan-inc%2FBaichuan2-7B-Chat%5D(%2Fbaichuan-inc%2FBaichuan2-7B-Chat)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kenken999/fastapi_django_main_live, lightmate/llm-chatbot, malvika2003/openvino_notebooks, officialhimanshu595/llama-factory, xiaoxishui/LAGENT, yhavinga/dutch-tokenizer-arena",13
+JiunYi/Baichuan2-7B-Chat-DcardStylePost-SFT,"---
+language:
+- zh
+license: gpl-3.0
+tags:
+- art
+- marketing
+- llama-factory
+base_model: baichuan-inc/Baichuan2-7B-Chat
+metrics:
+- bleu
+pipeline_tag: text-generation
+---
+
+
+## Baichuan2-7B-Chat-DcardStylePost-SFT
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = ""JiunYi/Baichuan2-7B-Chat-DcardStylePost-SFT""
+messages = [{""role"": ""user"", ""content"": ""【任务】：\n你是口碑行销公司的资深写手，现在要撰写一篇Dcard 『闲聊版』的口碑文。\n麻烦熟读下列 【参考资料】将商品或品牌与生活情境的融合，并尽量贴近生活口吻，以不要被发现是业配文为重要目标。\n请依照【口碑切角】为文章的故事主轴撰写整篇文章，请参考并依照【规范】角色口吻来撰写文章，融入角色情境来完成整篇故事撰写，请注重人物角色特色及限制。\n\n－\n\n【规范】：\n\n1.需产出文章标题\n2.请以第一人称方式撰写文章\n3.请记住现在是时间是西元 2023 年\n4.Please write in zh-TW language .\n5.降低口碑文业配感\n6.写作者是一个19~25岁之间的女生的大学生或是社会新鲜人\n7.喜爱吃甜食，常常带著男友一起去踩点试吃看看\n8.除了介绍食物，也喜欢顺便男友发生好笑的趣事来跟食物介绍的故事结合\n9.为降低口碑文业配感，请多多举几个甜点推荐\n10.喜欢应用很多可爱的颜文字和emoji\n\n－\n\n【参考资料】\n▲可丽露\n>>龙眼蜜，所以吃起来不会这么甜，跟其他家的可丽露吃起来真的很有差异\n以野生龙眼蜜减低并取代部分甜度,带出微微酸感的蛋蜜香,外脆内湿润的口感,完整的蜂巢组织度,木质调的兰姆酒香,法国盐之花平衡了整体,经典细致的马达加斯加香草籽原味,请在出炉后的3小时内食用完毕或\""冷冻\""保存,回烤后食用最接近现烤口感!\n\n\n\n▲奶盖布丁\n>>法国盐之花，连盐巴都很用心的甜点师\n带咸度的法国盐之花奶盖,微甜浓郁而不腻口的布蕾布丁体,和著偏苦的手煮焦糖液,是一款有著丰富层次的大人味布丁! 图片为示意仅供参考,食用时请由上方挖到底,品尝完整风味~\n\n【口碑切角】\n男友就像金鱼一样，好像记忆都只有三秒，\n只有三秒就算了还说错很多很好笑的话XD\n我都会带甜点回去给男友吃～结果男友居然说玛莉露很好吃XD\n玛莉露是神奇宝贝，可丽露才是甜点啦!\n分享日常男友都会口误的甜点们""}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    ""text-generation"",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map=""auto"",
+)
+
+outputs = pipeline(prompt, max_new_tokens=512, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0][""generated_text""])
+```","{""id"": ""JiunYi/Baichuan2-7B-Chat-DcardStylePost-SFT"", ""author"": ""JiunYi"", ""sha"": ""87d1b59085ce3ebb94ed64471d2d42ccb89ed354"", ""last_modified"": ""2024-05-01 16:49:58+00:00"", ""created_at"": ""2024-05-01 11:06:55+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 9, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""baichuan"", ""feature-extraction"", ""art"", ""marketing"", ""llama-factory"", ""text-generation"", ""conversational"", ""custom_code"", ""zh"", ""base_model:baichuan-inc/Baichuan2-7B-Chat"", ""base_model:finetune:baichuan-inc/Baichuan2-7B-Chat"", ""license:gpl-3.0"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: baichuan-inc/Baichuan2-7B-Chat\nlanguage:\n- zh\nlicense: gpl-3.0\nmetrics:\n- bleu\npipeline_tag: text-generation\ntags:\n- art\n- marketing\n- llama-factory"", ""widget_data"": null, ""model_index"": null, ""config"": {""architectures"": [""BaichuanForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_baichuan.BaichuanConfig"", ""AutoModel"": ""modeling_baichuan.BaichuanForCausalLM"", ""AutoModelForCausalLM"": ""baichuan-inc/Baichuan2-7B-Chat--modeling_baichuan.BaichuanForCausalLM""}, ""model_type"": ""baichuan"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""chat_template"": ""{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<reserved_106>' + content + '<reserved_107>' }}{% elif message['role'] == 'assistant' %}{{ content }}{% endif %}{% endfor %}"", ""eos_token"": ""</s>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>""}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": ""modeling_baichuan.BaichuanForCausalLM"", ""pipeline_tag"": ""feature-extraction"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='configuration_baichuan.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_utils.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00007-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00008-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00009-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00010-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00011-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00012-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00013-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00014-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00015-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00016-of-00016.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='modeling_baichuan.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='quantizer.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenization_baichuan.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""BF16"": 7505973248}, ""total"": 7505973248}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-05-01 16:49:58+00:00"", ""cardData"": ""base_model: baichuan-inc/Baichuan2-7B-Chat\nlanguage:\n- zh\nlicense: gpl-3.0\nmetrics:\n- bleu\npipeline_tag: text-generation\ntags:\n- art\n- marketing\n- llama-factory"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": ""modeling_baichuan.BaichuanForCausalLM"", ""pipeline_tag"": ""feature-extraction"", ""processor"": null}, ""_id"": ""6632224fb1c6e12e1c60161c"", ""modelId"": ""JiunYi/Baichuan2-7B-Chat-DcardStylePost-SFT"", ""usedStorage"": 15013973891}",1,,0,,0,https://huggingface.co/mradermacher/Baichuan2-7B-Chat-DcardStylePost-SFT-GGUF,1,,0,huggingface/InferenceSupport/discussions/new?title=JiunYi/Baichuan2-7B-Chat-DcardStylePost-SFT&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BJiunYi%2FBaichuan2-7B-Chat-DcardStylePost-SFT%5D(%2FJiunYi%2FBaichuan2-7B-Chat-DcardStylePost-SFT)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Cerebras-GPT-13B_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,184 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+cerebras/Cerebras-GPT-13B,"---
+language:
+- en
+inference: false
+tags:
+- pytorch
+- causal-lm
+license: apache-2.0
+datasets:
+- the_pile
+pipeline_tag: text-generation
+---
+
+# Cerebras-GPT 13B
+Check out our [Blog Post](https://www.cerebras.net/cerebras-gpt) and [arXiv paper](https://arxiv.org/abs/2304.03208)!
+
+## Model Description
+
+The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and scalability of training LLMs on the Cerebras software and hardware stack. All Cerebras-GPT models are available on Hugging Face.
+
+The family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models.
+
+All models in the Cerebras-GPT family have been trained in accordance with [Chinchilla scaling laws](https://arxiv.org/abs/2203.15556) (20 tokens per model parameter) which is compute-optimal.
+
+These models were trained on the [Andromeda](https://www.cerebras.net/andromeda/) AI supercomputer comprised of 16 CS-2 wafer scale systems. Cerebras' [weight streaming technology](https://www.cerebras.net/blog/linear-scaling-made-possible-with-weight-streaming) simplifies the training of LLMs by disaggregating compute from model storage. This allowed for efficient scaling of training across nodes using simple data parallelism.
+
+Cerebras systems for pre-training and fine tuning are available in the cloud via the [Cerebras Model Studio](https://www.cerebras.net/product-cloud/). Cerebras CS-2 compatible checkpoints are available in [Cerebras Model Zoo](https://github.com/Cerebras/modelzoo).
+
+## Model Details
+* Developed by: [Cerebras Systems](https://www.cerebras.net/)
+* License: Apache 2.0
+* Model type: Transformer-based Language Model
+* Architecture: GPT-3 style architecture
+* Data set: The Pile
+* Tokenizer: Byte Pair Encoding
+* Vocabulary Size: 50257
+* Sequence Length: 2048
+* Optimizer: AdamW, (β1, β2) = (0.9, 0.95), adam_eps = 1e−8 (1e−9 for larger models)
+* Positional Encoding: Learned
+* Language: English
+* Learn more: Dense Scaling Laws Paper for training procedure, config files, and details on how to use.
+
+**Contact**: To ask questions about Cerebras-GPT models, join the [Cerebras Discord](https://discord.gg/q6bZcMWJVu).
+
+This is the standard parameterization version of Cerebras-GPT with **13B** parameters
+
+Related models: [Cerebras-GPT Models](https://huggingface.co/models?sort=downloads&search=cerebras-gpt)
+
+<br><br>
+
+| Model         | Parameters | Layers | d_model | Heads | d_head | d_ffn  | LR       | BS (seq) | BS (tokens)     |
+|---------------|------------|--------|---------|-------|--------|--------|----------|----------|----------------|
+| Cerebras-GPT  | 111M       | 10     | 768     | 12    | 64     | 3072   | 6.0E-04 | 120      | 246K           |
+| Cerebras-GPT  | 256M       | 14     | 1088    | 17    | 64     | 4352   | 6.0E-04 | 264      | 541K           |
+| Cerebras-GPT  | 590M       | 18     | 1536    | 12    | 128    | 6144   | 2.0E-04 | 264      | 541K           |
+| Cerebras-GPT  | 1.3B       | 24     | 2048    | 16    | 128    | 8192   | 2.0E-04 | 528      | 1.08M          |
+| Cerebras-GPT  | 2.7B       | 32     | 2560    | 32    | 80     | 10240  | 2.0E-04 | 528      | 1.08M          |
+| Cerebras-GPT  | 6.7B       | 32     | 4096    | 32    | 128    | 16384  | 1.2E-04 | 1040     | 2.13M          |
+| Cerebras-GPT  | 13B        | 40     | 5120    | 40    | 128    | 20480  | 1.2E-04 | 720 &rarr; 1080 | 1.47M &rarr; 2.21M    |
+
+<br><br>
+
+## Quickstart 
+
+This model can be easily loaded using the AutoModelForCausalLM functionality:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained(""cerebras/Cerebras-GPT-13B"")
+model = AutoModelForCausalLM.from_pretrained(""cerebras/Cerebras-GPT-13B"")
+
+text = ""Generative AI is ""
+```
+
+And can be used with Hugging Face Pipelines
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(""text-generation"", model=model, tokenizer=tokenizer)
+generated_text = pipe(text, max_length=50, do_sample=False, no_repeat_ngram_size=2)[0]
+print(generated_text['generated_text'])
+```
+
+or with `model.generate()`
+
+```python
+inputs = tokenizer(text, return_tensors=""pt"")
+outputs = model.generate(**inputs, num_beams=5, 
+                        max_new_tokens=50, early_stopping=True,
+                        no_repeat_ngram_size=2)
+text_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+print(text_output[0])
+```
+<br><br>
+
+## Training data
+
+Cerebras-GPT is trained using [the Pile](https://pile.eleuther.ai) dataset from [EleutherAI](https://www.eleuther.ai). See the [Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed breakdown of data sources and methodology. The Pile was cleaned using the ftfy library to normalize the text, then filtered using scripts provided by Eleuther.
+
+We tokenized the data using byte-pair encoding using the GPT-2 vocabulary. Our tokenized version of the Pile has 371B tokens. We include more details about the training dataset preprocessing in Appendix A.1 of our paper.
+
+Recent works find significant duplicate data present in the Pile. Eleuther’s Pythia applies a deduplication process to reduce replicated data, decreasing the Pile dataset size. Pythia was trained on both the standard dataset and deduplicated dataset to characterize the impact. Our models are trained on the standard Pile without deduplication, which may present an opportunity for further improvement with the deduplicated data set.
+
+<br><br>
+
+## Training procedure
+
+We use the GPT-3 style model architecture. All of our layers use full attention as opposed to the GPT-3 style sparse banded attention. The model shapes were selected to either follow aspect ratio 80 or are the same shape as GPT-3 models. Learning rate warmed up for 375M tokens (1500 steps for 111M and 256M models) and 10x cosine decayed. No dropout was used and weight decay was set to 0.1. All models are trained with MSL of 2048.
+
+All models were trained to Chinchilla point: 20 tokens per model parameter. Number of steps was chosen based on optimal batch size (varied by model) and fixed sequence length (2048). See Training Table, below, for details. 
+
+<br>
+
+Model Params | Sequence Length | Batch Size | Number of Steps | Tokens | Tokens per Parameter | Flops
+------------ | -------------- | ---------- | --------------- | ------ | -------------------- | -----
+111M         | 2048           | 120        | 9037            | 2.22E+09 | 20                  | 2.6E+18
+256M         | 2048           | 264        | 9468            | 5.12E+09 | 20                  | 1.3E+19
+590M         | 2048           | 264        | 21836           | 1.18E+10 | 20                  | 6.1E+19
+1.3B         | 2048           | 528        | 24334           | 2.63E+10 | 20                  | 2.8E+20
+2.7B         | 2048           | 528        | 49041           | 5.30E+10 | 20                  | 1.1E+21
+6.7B         | 2048           | 1040       | 62522           | 1.33E+11 | 20                  | 6.3E+21
+13B          | 2048           | 720        | 174335          | 2.57E+11 | 20                  | 2.3E+22
+
+<br><br>
+
+## Evaluations
+
+We trained models from smallest to largest and fit a power law as we went along. The power law was helpful for extrapolating the validation loss of the next largest model we trained and provided confidence about whether the training run was going well.
+
+We performed upstream (pre-training) evaluations of text prediction cross-entropy using the Pile validation and test splits. We performed downstream evaluations of text generation accuracy on standardized tasks using the [Eleuther lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). Results are compared against many publicly available large language models in Section 3 of the paper.
+
+
+#### 0-shot Evaluation
+| Model   | Params | Training FLOPs | PILE test xent | Hella-Swag | PIQA  | Wino-Grande | Lambada | ARC-e | ARC-c | OpenBookQA | Downstream Average |
+| ------- | ----- | -------------- | -------------- | ---------- | ----- | ----------- | ------- | ----- | ----- | ---------- | ------------------ |
+| Cerebras-GPT | 111M  | 2.6E+18        | 2.566          | 0.268      | 0.594 | 0.488       | 0.194   | 0.380 | 0.166 | 0.118      | 0.315              |
+| Cerebras-GPT | 256M  | 1.3E+19        | 2.299          | 0.274      | 0.613 | 0.511       | 0.293   | 0.410 | 0.170 | 0.158      | 0.347              |
+| Cerebras-GPT | 590M  | 6.1E+19        | 2.184          | 0.291      | 0.627 | 0.498       | 0.366   | 0.464 | 0.190 | 0.158      | 0.370              |
+| Cerebras-GPT | 1.3B  | 2.8E+20        | 1.996          | 0.325      | 0.664 | 0.521       | 0.462   | 0.508 | 0.224 | 0.166      | 0.410              |
+| Cerebras-GPT | 2.7B  | 1.1E+21        | 1.834          | 0.386      | 0.701 | 0.559       | 0.567   | 0.571 | 0.246 | 0.206      | 0.462              |
+| Cerebras-GPT | 6.7B  | 6.3E+21        | 1.704          | 0.447      | 0.739 | 0.602       | 0.636   | 0.643 | 0.282 | 0.238      | 0.512              |
+| Cerebras-GPT | 13B   | 2.3E+22        | 1.575          | 0.513      | 0.766 | 0.646       | 0.696   | 0.714 | 0.367 | 0.286      | 0.570              |
+
+#### 5-shot Evaluation
+| Model    | Params | Hella-Swag | PIQA  | Wino-Grande | Lambada | ARC-e | ARC-c | OpenBookQA |
+| -------- | ----- | ----------| ----- | ----------- | -------| ----- | ----- | ---------- |
+| Cerebras-GPT | 111M  | 0.267     | 0.588 | 0.475       | 0.158  | 0.356 | 0.166 | 0.136      |
+| Cerebras-GPT | 256M  | 0.278     | 0.606 | 0.522       | 0.225  | 0.422 | 0.183 | 0.164      |
+| Cerebras-GPT | 590M  | 0.291     | 0.634 | 0.479       | 0.281  | 0.475 | 0.206 | 0.152      |
+| Cerebras-GPT | 1.3B  | 0.326     | 0.668 | 0.536       | 0.395  | 0.529 | 0.241 | 0.174      |
+| Cerebras-GPT | 2.7B  | 0.382     | 0.697 | 0.543       | 0.487  | 0.590 | 0.267 | 0.224      |
+| Cerebras-GPT | 6.7B  | 0.444     | 0.736 | 0.590       | 0.591  | 0.667 | 0.314 | 0.270      |
+| Cerebras-GPT | 13B   | 0.514     | 0.768 | 0.674       | 0.655  | 0.743 | 0.398 | 0.318      |
+
+
+<br><br>
+
+## Uses and Limitations
+
+### Intended Use
+The primary intended use is to further research into large language models. These models can be used as a foundation model for NLP, applications, ethics, and alignment research. Our primary intended users are researchers who are working to improve LLMs and practitioners seeking reference implementations, training setups, hyperparameters, or pre-trained models. We release these models with a fully permissive Apache license for the community to use freely.
+
+You may fine-tune and adapt Cerebras-GPT models for deployment via either Cerebras [Model Studio](https://www.cerebras.net/product-cloud/) or third-party libraries. Further safety-related testing and mitigations should be applied beore using the Cerebras-GPT model family in production downstream applications. 
+
+Due to financial and compute budgets, Cerebras-GPT models were only trained and evaluated following the approaches described in the paper.
+
+### Out of Scope Use
+Cerebras-GPT models are trained on the Pile, with English language only, and are not suitable for machine translation tasks.
+
+Cerebras-GPT models have not been tuned for human-facing dialog applications like chatbots and will not respond to prompts in a similar way to models that have received instruction tuning or reinforcement learning from human feedback (RLHF) like Flan-T5 or ChatGPT. Cerebras-GPT models can be tuned using those methods.
+
+### Risk, Bias, Ethical Considerations
+* **Data**: The Pile dataset has been thoroughly analyzed from various ethical standpoints such as toxicity analysis, gender bias, pejorative content, racially sensitive content etc. Please refer to Pile dataset references.
+* **Human life**: The outputs from this model may or may not align with human values. The risk needs to be thoroughly investigated before deploying this model in a production environment where it can directly impact human life.
+* **Risks and harms**: There can be distributional bias in the Pile dataset that can manifest in various forms in the downstream model deployment. There are other risks associated with large language models such as amplifying stereotypes, memorizing training data, or revealing private or secure information.
+* **Mitigations**: Only mitigations in standard Pile dataset pre-processing were employed when pre-training Cerebras-GPT.
+
+<br><br>
+
+## Acknowledgements
+
+We are thankful to all Cerebras engineers, past and present, that made this work possible.","{""id"": ""cerebras/Cerebras-GPT-13B"", ""author"": ""cerebras"", ""sha"": ""0600d825e90eaf087b013c4146c1d7d460b831c0"", ""last_modified"": ""2023-11-22 21:49:12+00:00"", ""created_at"": ""2023-03-20 20:45:54+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 588, ""downloads_all_time"": null, ""likes"": 646, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""gpt2"", ""feature-extraction"", ""causal-lm"", ""text-generation"", ""en"", ""dataset:the_pile"", ""arxiv:2304.03208"", ""arxiv:2203.15556"", ""arxiv:2101.00027"", ""license:apache-2.0"", ""text-generation-inference"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""datasets:\n- the_pile\nlanguage:\n- en\nlicense: apache-2.0\npipeline_tag: text-generation\ntags:\n- pytorch\n- causal-lm\ninference: false"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""GPT2Model""], ""model_type"": ""gpt2""}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": ""feature-extraction"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00002.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00002.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""eduagarcia/open_pt_llm_leaderboard"", ""Intel/low_bit_open_llm_leaderboard"", ""BAAI/open_cn_llm_leaderboard"", ""Sharathhebbar24/One-stop-for-Open-source-models"", ""gsaivinay/open_llm_leaderboard"", ""aimevzulari/Prompt_Uzmani"", ""GTBench/GTBench"", ""Vikhrmodels/small-shlepa-lb"", ""kz-transformers/kaz-llm-lb"", ""felixz/open_llm_leaderboard"", ""OPTML-Group/UnlearnCanvas-Benchmark"", ""BAAI/open_flageval_vlm_leaderboard"", ""b1sheng/kg_llm_leaderboard_test"", ""neubla/neubla-llm-evaluation-board"", ""rodrigomasini/data_only_open_llm_leaderboard"", ""Docfile/open_llm_leaderboard"", ""shaoyie/Cerebras"", ""smothiki/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard_two"", ""wissamantoun/LLM_Detection_Attribution"", ""0x1668/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard-check"", ""asir0z/open_llm_leaderboard"", ""kbmlcoding/open_llm_leaderboard_free"", ""K00B404/One-stop-till-you-drop"", ""aichampions/open_llm_leaderboard"", ""Adeco/open_llm_leaderboard"", ""anirudh937/open_llm_leaderboard"", ""smothiki/open_llm_leaderboard2"", ""mjalg/IFEvalTR"", ""therayz1/Prompt_Engineer"", ""kayrahan/promtmuhendisi""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-11-22 21:49:12+00:00"", ""cardData"": ""datasets:\n- the_pile\nlanguage:\n- en\nlicense: apache-2.0\npipeline_tag: text-generation\ntags:\n- pytorch\n- causal-lm\ninference: false"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": ""feature-extraction"", ""processor"": ""AutoTokenizer""}, ""_id"": ""6418c60283d469238a51306f"", ""modelId"": ""cerebras/Cerebras-GPT-13B"", ""usedStorage"": 103162871514}",0,,0,,0,,0,,0,"BAAI/open_cn_llm_leaderboard, BAAI/open_flageval_vlm_leaderboard, GTBench/GTBench, Intel/low_bit_open_llm_leaderboard, OPTML-Group/UnlearnCanvas-Benchmark, Sharathhebbar24/One-stop-for-Open-source-models, Vikhrmodels/small-shlepa-lb, aimevzulari/Prompt_Uzmani, eduagarcia/open_pt_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=cerebras/Cerebras-GPT-13B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bcerebras%2FCerebras-GPT-13B%5D(%2Fcerebras%2FCerebras-GPT-13B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kayrahan/promtmuhendisi, therayz1/Prompt_Engineer, wissamantoun/LLM_Detection_Attribution",13

ChatTTS_finetunes_20250424_223250.csv_finetunes_20250424_223250.csv

@@ -0,0 +1,56 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+2Noise/ChatTTS,"---
+license: cc-by-nc-4.0
+library_name: chat_tts
+pipeline_tag: text-to-audio
+---
+
+
+**We are also training larger-scale models and need computational power and data support. If you can provide assistance, please contact OPEN-SOURCE@2NOISE.COM. Thank you very much.**
+
+## Clone the Repository
+First, clone the Git repository:
+```bash
+git clone https://github.com/2noise/ChatTTS.git
+```
+
+## Model Inference
+
+
+```python
+# Import necessary libraries and configure settings
+import torch
+import torchaudio
+torch._dynamo.config.cache_size_limit = 64
+torch._dynamo.config.suppress_errors = True
+torch.set_float32_matmul_precision('high')
+
+import ChatTTS
+from IPython.display import Audio
+
+# Initialize and load the model: 
+chat = ChatTTS.Chat()
+chat.load_models(compile=False) # Set to True for better performance
+
+# Define the text input for inference (Support Batching)
+texts = [
+    ""So we found being competitive and collaborative was a huge way of staying motivated towards our goals, so one person to call when you fall off, one person who gets you back on then one person to actually do the activity with."",
+    ]
+
+# Perform inference and play the generated audio
+wavs = chat.infer(texts)
+Audio(wavs[0], rate=24_000, autoplay=True)
+
+# Save the generated audio 
+torchaudio.save(""output.wav"", torch.from_numpy(wavs[0]), 24000)
+```
+**For more usage examples, please refer to the [example notebook](https://github.com/2noise/ChatTTS/blob/main/example.ipynb), which includes parameters for finer control over the generated speech, such as specifying the speaker, adjusting speech speed, and adding laughter.**
+
+
+
+
+
+
+### Disclaimer: For Academic Purposes Only
+
+The information provided in this document is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes. The authors do not guarantee the accuracy, completeness, or reliability of the information.","{""id"": ""2Noise/ChatTTS"", ""author"": ""2Noise"", ""sha"": ""1a3c04a8b0651689bd9242fbb55b1f4b5a9aef84"", ""last_modified"": ""2024-10-22 08:26:20+00:00"", ""created_at"": ""2024-05-25 06:07:38+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 2918, ""downloads_all_time"": null, ""likes"": 1551, ""library_name"": ""chat_tts"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""chat_tts"", ""safetensors"", ""text-to-audio"", ""license:cc-by-nc-4.0"", ""region:us""], ""pipeline_tag"": ""text-to-audio"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""library_name: chat_tts\nlicense: cc-by-nc-4.0\npipeline_tag: text-to-audio"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/DVAE.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/DVAE.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/DVAE_full.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/Decoder.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/Decoder.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/Embed.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/GPT.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/Vocos.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/Vocos.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/gpt/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/gpt/model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/spk_stat.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/tokenizer.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/tokenizer/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/tokenizer/tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='asset/tokenizer/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config/decoder.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config/dvae.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config/gpt.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config/path.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config/vocos.yaml', size=None, blob_id=None, lfs=None)""], ""spaces"": [""Dzkaka/ChatTTS"", ""lenML/ChatTTS-Forge"", ""Hilley/ChatTTS-OpenVoice"", ""markmagic/ChatTTS"", ""Hilley/ChatVC"", ""wffcyrus/ChatTTS-Story-Telling"", ""6Simple9/ChatTTS-OpenVoice"", ""fcyai/ChatTTS"", ""rao223/ChatTTS-Forge"", ""chenmgtea/chat-tts"", ""doby4u/chattts"", ""cbhhhcb/ChatTTS"", ""zelk12/ChatTTS-Forge_English_interface"", ""rainnee0925/ChatTTS"", ""prajjwalkapoor/tts"", ""fcyai/ChatTTS-Story-Telling"", ""sysf/ChatTTS"", ""savokiss/ChatTTS"", ""savokiss/chattts-free"", ""arpy8/chattts"", ""lisongfeng/ChatTTS-WebUI"", ""rainnee/ChatTTS"", ""wffcyrus/ChatTTS-Forge"", ""zzhouz/learningself"", ""docs4dev/gptalk"", ""sandy-try/ChatTTS-Forge"", ""slingkid/ChatVC2"", ""panyanyany/ChatTTS"", ""slingkid/ChatVC4"", ""Rdtuetr/ChatTTS"", ""hikerxu/ChatTTS"", ""zhzabcd/ChatTTS-Forge"", ""jdhsi/ChatTTS"", ""AwesomeK/ChatTTS-OpenVoice"", ""zhengr/ChatTTS-Forge"", ""zhengr/ChatTTS2"", ""weismart1807/Linly-Talker"", ""emilalvaro/ChatTTS-OpenVoice"", ""vuxuanhoan/ChatTTS-Forge"", ""MaktubCN/Chat-TTS"", ""EZMODEL/chattts-free"", ""chenjacky131/ChatTTS-Forge"", ""lalalic/chattts"", ""yamazing/ChatTTS"", ""zhangyanhua0913/ChatTTS-OpenVoice"", ""thierryguyot67/tts"", ""EagleW96/CC_ChatTTS_demo""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-10-22 08:26:20+00:00"", ""cardData"": ""library_name: chat_tts\nlicense: cc-by-nc-4.0\npipeline_tag: text-to-audio"", ""transformersInfo"": null, ""_id"": ""6651802a815d7642d5aaef56"", ""modelId"": ""2Noise/ChatTTS"", ""usedStorage"": 2364745599}",0,,0,,0,,0,,0,"6Simple9/ChatTTS-OpenVoice, Dzkaka/ChatTTS, EagleW96/CC_ChatTTS_demo, Hilley/ChatTTS-OpenVoice, Hilley/ChatVC, cbhhhcb/ChatTTS, huggingface/InferenceSupport/discussions/546, lenML/ChatTTS-Forge, panyanyany/ChatTTS, rao223/ChatTTS-Forge, savokiss/chattts-free, wffcyrus/ChatTTS-Forge, zelk12/ChatTTS-Forge_English_interface",13

CogVideoX-5b-I2V_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,281 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+THUDM/CogVideoX-5b-I2V,"---
+license: other
+license_link: https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE
+language:
+  - en
+tags:
+  - cogvideox
+  - video-generation
+  - thudm
+  - image-to-video
+inference: false
+---
+
+# CogVideoX-5B-I2V
+
+<p style=""text-align: center;"">
+  <div align=""center"">
+  <img src=https://github.com/THUDM/CogVideo/raw/main/resources/logo.svg width=""50%""/>
+  </div>
+  <p align=""center"">
+  <a href=""https://huggingface.co/THUDM//CogVideoX-5b-I2V/blob/main/README.md"">📄 Read in English</a> | 
+  <a href=""https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space"">🤗 Huggingface Space</a> |
+  <a href=""https://github.com/THUDM/CogVideo"">🌐 Github </a> | 
+  <a href=""https://arxiv.org/pdf/2408.06072"">📜 arxiv </a>
+</p>
+<p align=""center"">
+📍 Visit <a href=""https://chatglm.cn/video?fr=osm_cogvideox"">Qingying</a> and <a href=""https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9"">API Platform</a> for the commercial version of the video generation model
+</p>
+
+## Model Introduction
+
+CogVideoX is an open-source video generation model originating
+from [Qingying](https://chatglm.cn/video?fr=osm_cogvideo). The table below presents information related to the video
+generation models we offer in this version.
+
+<table  style=""border-collapse: collapse; width: 100%;"">
+  <tr>
+    <th style=""text-align: center;"">Model Name</th>
+    <th style=""text-align: center;"">CogVideoX-2B</th>
+    <th style=""text-align: center;"">CogVideoX-5B</th>
+    <th style=""text-align: center;"">CogVideoX-5B-I2V (This Repository)</th>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Model Description</td>
+    <td style=""text-align: center;"">Entry-level model, balancing compatibility. Low cost for running and secondary development.</td>
+    <td style=""text-align: center;"">Larger model with higher video generation quality and better visual effects.</td>
+    <td style=""text-align: center;"">CogVideoX-5B image-to-video version.</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Inference Precision</td>
+    <td style=""text-align: center;""><b>FP16*(recommended)</b>, BF16, FP32, FP8*, INT8, not supported: INT4</td>
+    <td colspan=""2"" style=""text-align: center;""><b>BF16 (recommended)</b>, FP16, FP32, FP8*, INT8, not supported: INT4</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Single GPU Memory Usage<br></td>
+    <td style=""text-align: center;""><a href=""https://github.com/THUDM/SwissArmyTransformer"">SAT</a> FP16: 18GB <br><b>diffusers FP16: from 4GB* </b><br><b>diffusers INT8 (torchao): from 3.6GB*</b></td>
+    <td colspan=""2"" style=""text-align: center;""><a href=""https://github.com/THUDM/SwissArmyTransformer"">SAT</a> BF16: 26GB <br><b>diffusers BF16: from 5GB* </b><br><b>diffusers INT8 (torchao): from 4.4GB*</b></td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Multi-GPU Inference Memory Usage</td>
+    <td style=""text-align: center;""><b>FP16: 10GB* using diffusers</b><br></td>
+    <td colspan=""2"" style=""text-align: center;""><b>BF16: 15GB* using diffusers</b><br></td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Inference Speed<br>(Step = 50, FP/BF16)</td>
+    <td style=""text-align: center;"">Single A100: ~90 seconds<br>Single H100: ~45 seconds</td>
+    <td colspan=""2"" style=""text-align: center;"">Single A100: ~180 seconds<br>Single H100: ~90 seconds</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Fine-tuning Precision</td>
+    <td style=""text-align: center;""><b>FP16</b></td>
+    <td colspan=""2"" style=""text-align: center;""><b>BF16</b></td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Fine-tuning Memory Usage</td>
+    <td style=""text-align: center;"">47 GB (bs=1, LORA)<br> 61 GB (bs=2, LORA)<br> 62GB (bs=1, SFT)</td>
+    <td style=""text-align: center;"">63 GB (bs=1, LORA)<br> 80 GB (bs=2, LORA)<br> 75GB (bs=1, SFT)<br></td>
+    <td style=""text-align: center;"">78 GB (bs=1, LORA)<br> 75GB (bs=1, SFT, 16GPU)<br></td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Prompt Language</td>
+    <td colspan=""3"" style=""text-align: center;"">English*</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Maximum Prompt Length</td>
+    <td colspan=""3"" style=""text-align: center;"">226 Tokens</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Video Length</td>
+    <td colspan=""3"" style=""text-align: center;"">6 Seconds</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Frame Rate</td>
+    <td colspan=""3"" style=""text-align: center;"">8 Frames / Second</td>
+  </tr>
+  <tr>
+    <td style=""text-align: center;"">Video Resolution</td>
+    <td colspan=""3"" style=""text-align: center;"">720 x 480, no support for other resolutions (including fine-tuning)</td>
+  </tr>
+    <tr>
+    <td style=""text-align: center;"">Position Embedding</td>
+    <td style=""text-align: center;"">3d_sincos_pos_embed</td>
+    <td style=""text-align: center;"">3d_rope_pos_embed</td>
+    <td style=""text-align: center;"">3d_rope_pos_embed + learnable_pos_embed</td>
+  </tr>
+</table>
+
+**Data Explanation**
+
++ While testing using the diffusers library, all optimizations included in the diffusers library were enabled. This
+  scheme has not been tested for actual memory usage on devices outside of **NVIDIA A100 / H100** architectures.
+  Generally, this scheme can be adapted to all **NVIDIA Ampere architecture** and above devices. If optimizations are
+  disabled, memory consumption will multiply, with peak memory usage being about 3 times the value in the table.
+  However, speed will increase by about 3-4 times. You can selectively disable some optimizations, including:
+
+```
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
+```
+
++ For multi-GPU inference, the `enable_sequential_cpu_offload()` optimization needs to be disabled.
++ Using INT8 models will slow down inference, which is done to accommodate lower-memory GPUs while maintaining minimal
+  video quality loss, though inference speed will significantly decrease.
++ The CogVideoX-2B model was trained in `FP16` precision, and all CogVideoX-5B models were trained in `BF16` precision.
+  We recommend using the precision in which the model was trained for inference.
++ [PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
+  used to quantize the text encoder, transformer, and VAE modules to reduce the memory requirements of CogVideoX. This
+  allows the model to run on free T4 Colabs or GPUs with smaller memory! Also, note that TorchAO quantization is fully
+  compatible with `torch.compile`, which can significantly improve inference speed. FP8 precision must be used on
+  devices with NVIDIA H100 and above, requiring source installation of `torch`, `torchao`, `diffusers`, and `accelerate`
+  Python packages. CUDA 12.4 is recommended.
++ The inference speed tests also used the above memory optimization scheme. Without memory optimization, inference speed
+  increases by about 10%. Only the `diffusers` version of the model supports quantization.
++ The model only supports English input; other languages can be translated into English for use via large model
+  refinement.
++ The memory usage of model fine-tuning is tested in an `8 * H100` environment, and the program automatically
+  uses `Zero 2` optimization. If a specific number of GPUs is marked in the table, that number or more GPUs must be used
+  for fine-tuning.
+
+**Reminders**
+
++ Use [SAT](https://github.com/THUDM/SwissArmyTransformer) for inference and fine-tuning SAT version models. Feel free
+  to visit our GitHub for more details.
+
+## Getting Started Quickly 🤗
+
+This model supports deployment using the Hugging Face diffusers library. You can follow the steps below to get started.
+
+**We recommend that you visit our [GitHub](https://github.com/THUDM/CogVideo) to check out prompt optimization and
+conversion to get a better experience.**
+
+1. Install the required dependencies
+
+```shell
+# diffusers>=0.30.3
+# transformers>=0.44.2
+# accelerate>=0.34.0
+# imageio-ffmpeg>=0.5.1
+pip install --upgrade transformers accelerate diffusers imageio-ffmpeg 
+```
+
+2. Run the code
+
+```python
+import torch
+from diffusers import CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+
+prompt = ""A little girl is riding a bicycle at high speed. Focused, detailed, realistic.""
+image = load_image(image=""input.jpg"")
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    ""THUDM/CogVideoX-5b-I2V"",
+    torch_dtype=torch.bfloat16
+)
+
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+video = pipe(
+    prompt=prompt,
+    image=image,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=49,
+    guidance_scale=6,
+    generator=torch.Generator(device=""cuda"").manual_seed(42),
+).frames[0]
+
+export_to_video(video, ""output.mp4"", fps=8)
+```
+
+## Quantized Inference
+
+[PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
+used to quantize the text encoder, transformer, and VAE modules to reduce CogVideoX's memory requirements. This allows
+the model to run on free T4 Colab or GPUs with lower VRAM! Also, note that TorchAO quantization is fully compatible
+with `torch.compile`, which can significantly accelerate inference.
+
+```
+# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
+# Source and nightly installation is only required until the next release.
+
+import torch
+from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import T5EncoderModel
+from torchao.quantization import quantize_, int8_weight_only
+
+quantization = int8_weight_only
+
+text_encoder = T5EncoderModel.from_pretrained(""THUDM/CogVideoX-5b-I2V"", subfolder=""text_encoder"", torch_dtype=torch.bfloat16)
+quantize_(text_encoder, quantization())
+
+transformer = CogVideoXTransformer3DModel.from_pretrained(""THUDM/CogVideoX-5b-I2V"",subfolder=""transformer"", torch_dtype=torch.bfloat16)
+quantize_(transformer, quantization())
+
+vae = AutoencoderKLCogVideoX.from_pretrained(""THUDM/CogVideoX-5b-I2V"", subfolder=""vae"", torch_dtype=torch.bfloat16)
+quantize_(vae, quantization())
+
+# Create pipeline and run inference
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    ""THUDM/CogVideoX-5b-I2V"",
+    text_encoder=text_encoder,
+    transformer=transformer,
+    vae=vae,
+    torch_dtype=torch.bfloat16,
+)
+
+pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+prompt = ""A little girl is riding a bicycle at high speed. Focused, detailed, realistic.""
+image = load_image(image=""input.jpg"")
+video = pipe(
+    prompt=prompt,
+    image=image,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=49,
+    guidance_scale=6,
+    generator=torch.Generator(device=""cuda"").manual_seed(42),
+).frames[0]
+
+export_to_video(video, ""output.mp4"", fps=8)
+```
+
+Additionally, these models can be serialized and stored using PytorchAO in quantized data types to save disk space. You
+can find examples and benchmarks at the following links:
+
+- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
+- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
+
+## Further Exploration
+
+Feel free to enter our [GitHub](https://github.com/THUDM/CogVideo), where you'll find:
+
+1. More detailed technical explanations and code.
+2. Optimized prompt examples and conversions.
+3. Detailed code for model inference and fine-tuning.
+4. Project update logs and more interactive opportunities.
+5. CogVideoX toolchain to help you better use the model.
+6. INT8 model inference code.
+
+## Model License
+
+This model is released under the [CogVideoX LICENSE](LICENSE).
+
+## Citation
+
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}
+```","{""id"": ""THUDM/CogVideoX-5b-I2V"", ""author"": ""THUDM"", ""sha"": ""a6f0f4858a8395e7429d82493864ce92bf73af11"", ""last_modified"": ""2024-11-23 06:34:36+00:00"", ""created_at"": ""2024-09-16 02:57:53+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 88546, ""downloads_all_time"": null, ""likes"": 288, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""cogvideox"", ""video-generation"", ""thudm"", ""image-to-video"", ""en"", ""arxiv:2408.06072"", ""license:other"", ""diffusers:CogVideoXImageToVideoPipeline"", ""region:us""], ""pipeline_tag"": ""image-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\nlicense: other\nlicense_link: https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE\ntags:\n- cogvideox\n- video-generation\n- thudm\n- image-to-video\ninference: false"", ""widget_data"": null, 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ControlNetMediaPipeFace_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,213 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+CrucibleAI/ControlNetMediaPipeFace,"---
+language:
+- en
+thumbnail: ''
+tags:
+- controlnet
+- laion
+- face
+- mediapipe
+- image-to-image
+license: openrail
+base_model: stabilityai/stable-diffusion-2-1-base
+datasets:
+- LAION-Face
+- LAION
+pipeline_tag: image-to-image
+---
+
+# ControlNet LAION Face Dataset
+
+## Table of Contents:
+- Overview: Samples, Contents, and Construction
+- Usage: Downloading, Training, and Inference
+- License
+- Credits and Thanks
+
+# Overview:
+
+This dataset is designed to train a ControlNet with human facial expressions.  It includes keypoints for pupils to allow gaze direction.  Training has been tested on Stable Diffusion v2.1 base (512) and Stable Diffusion v1.5.
+
+## Samples:
+
+Cherry-picked from ControlNet + Stable Diffusion v2.1 Base
+
+|Input|Face Detection|Output|
+|:---:|:---:|:---:|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/happy_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/happy_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/happy_result.png"">|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/neutral_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/neutral_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/neutral_result.png"">|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sad_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sad_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sad_result.png"">|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/screaming_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/screaming_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/screaming_result.png"">|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sideways_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sideways_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/sideways_result.png"">|
+|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/surprised_source.jpg"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/surprised_annotation.png"">|<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/surprised_result.png"">|
+
+Images with multiple faces are also supported:
+
+<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/family_source.jpg"">
+
+<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/family_annotation.png"">
+
+<img src=""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/family_result.png"">
+
+
+## Dataset Contents:
+
+- train_laion_face.py - Entrypoint for ControlNet training.
+- laion_face_dataset.py - Code for performing dataset iteration.  Cropping and resizing happens here.
+- tool_download_face_targets.py - A tool to read metadata.json and populate the target folder.
+- tool_generate_face_poses.py - The original file used to generate the source images.  Included for reproducibility, but not required for training.
+- training/laion-face-processed/prompt.jsonl - Read by laion_face_dataset.  Includes prompts for the images.
+- training/laion-face-processed/metadata.json - Excerpts from LAION for the relevant data.  Also used for downloading the target dataset.
+- training/laion-face-processed/source/xxxxxxxxx.jpg - Images with detections performed.  Generated from the target images.
+- training/laion-face-processed/target/xxxxxxxxx.jpg - Selected images from LAION Face.
+
+## Dataset Construction:
+
+Source images were generated by pulling slice 00000 from LAION Face and passing them through MediaPipe's face detector with special configuration parameters.  
+
+The colors and line thicknesses used for MediaPipe are as follows:
+
+```
+f_thick = 2
+f_rad = 1
+right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
+right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
+right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
+left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
+left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
+left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
+mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad)
+head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
+
+iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
+```
+
+We have implemented a method named `draw_pupils` which modifies some functionality from MediaPipe.  It exists as a stopgap until some pending changes are merged.
+
+
+# Usage:
+
+The containing ZIP file should be decompressed into the root of the ControlNet directory.  The `train_laion_face.py`, `laion_face_dataset.py`, and other `.py` files should sit adjacent to `tutorial_train.py` and `tutorial_train_sd21.py`.  We are assuming a checkout of the ControlNet repo at 0acb7e5, but there is no direct dependency on the repository.
+
+## Downloading:
+
+For copyright reasons, we cannot include the original target files.  We have provided a script (tool_download_face_targets.py) which will read from training/laion-face-processed/metadata.json and populate the target folder.  This file has no requirements, but will use tqdm if it is installed.
+
+## Training:
+
+When the targets folder is fully populated, training can be run on a machine with at least 24 gigabytes of VRAM.  Our model was trained for 200 hours (four epochs) on an A6000.
+
+```bash
+python tool_add_control.py ./models/v1-5-pruned-emaonly.ckpt ./models/controlnet_sd15_laion_face.ckpt
+python ./train_laion_face_sd15.py
+```
+
+## Inference:
+
+We have provided `gradio_face2image.py`.  Update the following two lines to point them to your trained model.
+
+```
+model = create_model('./models/cldm_v21.yaml').cpu()  # If you fine-tune on SD2.1 base, this does not need to change.
+model.load_state_dict(load_state_dict('./models/control_sd21_openpose.pth', location='cuda'))
+```
+
+The model has some limitations: while it is empirically better at tracking gaze and mouth poses than previous attempts, it may still ignore controls.  Adding details to the prompt like, ""looking right"" can abate bad behavior. 
+
+## 🧨 Diffusers
+
+It is recommended to use the checkpoint with [Stable Diffusion 2.1 - Base](stabilityai/stable-diffusion-2-1-base) as the checkpoint has been trained on it.
+Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.
+
+To use with Stable Diffusion 1.5, insert `subfolder=""diffusion_sd15""` into the from_pretrained arguments.  A v1.5 half-precision variant is provided but untested.
+
+1. Install `diffusers` and related packages:
+```
+$ pip install diffusers transformers accelerate
+```
+
+2. Run code:
+```py
+from PIL import Image
+import numpy as np
+import torch
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+from diffusers.utils import load_image
+
+image = load_image(
+    ""https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/samples_laion_face_dataset/family_annotation.png""
+)
+
+# Stable Diffusion 2.1-base:
+controlnet = ControlNetModel.from_pretrained(""CrucibleAI/ControlNetMediaPipeFace"", torch_dtype=torch.float16, variant=""fp16"")
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+	""stabilityai/stable-diffusion-2-1-base"", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+)
+# OR
+# Stable Diffusion 1.5:
+controlnet = ControlNetModel.from_pretrained(""CrucibleAI/ControlNetMediaPipeFace"", subfolder=""diffusion_sd15"")
+pipe = StableDiffusionControlNetPipeline.from_pretrained(""runwayml/stable-diffusion-v1-5"", controlnet=controlnet, safety_checker=None)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# Remove if you do not have xformers installed
+# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
+# for installation instructions
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_model_cpu_offload()
+
+image = pipe(""a happy family at a dentist advertisement"", image=image, num_inference_steps=30).images[0]
+image.save('./images.png')
+```
+
+
+# License:
+
+### Source Images: (/training/laion-face-processed/source/)
+This work is marked with CC0 1.0. To view a copy of this license, visit http://creativecommons.org/publicdomain/zero/1.0
+
+### Trained Models:
+Our trained ControlNet checkpoints are released under CreativeML Open RAIL-M.
+
+### Source Code:
+lllyasviel/ControlNet is licensed under the Apache License 2.0
+
+Our modifications are released under the same license.
+
+
+# Credits and Thanks:
+
+Greatest thanks to Zhang et al. for ControlNet, Rombach et al. (StabilityAI) for Stable Diffusion, and Schuhmann et al. for LAION.
+
+Sample images for this document were obtained from Unsplash and are CC0.
+
+```
+@misc{zhang2023adding,
+  title={Adding Conditional Control to Text-to-Image Diffusion Models}, 
+  author={Lvmin Zhang and Maneesh Agrawala},
+  year={2023},
+  eprint={2302.05543},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+
+@misc{rombach2021highresolution,
+      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
+      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
+      year={2021},
+      eprint={2112.10752},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{schuhmann2022laion5b,
+      title={LAION-5B: An open large-scale dataset for training next generation image-text models}, 
+      author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev},
+      year={2022},
+      eprint={2210.08402},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+This project was made possible by Crucible AI.","{""id"": ""CrucibleAI/ControlNetMediaPipeFace"", ""author"": ""CrucibleAI"", ""sha"": ""f6ed75cc495674bea8bf7409ef3d0e5bfb7d8c90"", ""last_modified"": ""2023-05-19 19:32:02+00:00"", ""created_at"": ""2023-03-30 18:28:07+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1595, ""downloads_all_time"": null, ""likes"": 566, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""controlnet"", ""laion"", ""face"", ""mediapipe"", ""image-to-image"", ""en"", ""dataset:LAION-Face"", ""dataset:LAION"", ""arxiv:2302.05543"", ""arxiv:2112.10752"", ""arxiv:2210.08402"", ""base_model:stabilityai/stable-diffusion-2-1-base"", ""base_model:adapter:stabilityai/stable-diffusion-2-1-base"", ""license:openrail"", ""region:us""], ""pipeline_tag"": ""image-to-image"", ""mask_token"": null, ""trending_score"": null, ""card_data"": 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Cyberpunk-Anime-Diffusion_finetunes_20250425_143346.csv_finetunes_20250425_143346.csv

@@ -0,0 +1,122 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+DGSpitzer/Cyberpunk-Anime-Diffusion,"---
+language: 
+  - en
+thumbnail: ""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/thumbnail.png""
+tags:
+- cyberpunk
+- anime
+- waifu-diffusion
+- stable-diffusion
+- aiart
+- text-to-image
+license: creativeml-openrail-m
+---
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/5.jpg"" width=""512"" height=""512""/></center>
+
+![visitors](https://visitor-badge.glitch.me/badge?page_id=Cyberpunk_Anime_Diffusion)
+
+# Cyberpunk Anime Diffusion
+
+An AI model that generates cyberpunk anime characters!~
+
+Based of a finetuned Waifu Diffusion V1.3 Model with Stable Diffusion V1.5 New Vae, training in Dreambooth
+
+by [DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg)
+
+### 🧨 Diffusers
+
+This repo contains both .ckpt and Diffuser model files. It's compatible to be used as any Stable Diffusion model, using standard [Stable Diffusion Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
+
+You can convert this model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](https://huggingface.co/blog/stable_diffusion_jax).
+
+```python example for loading the Diffuser
+#!pip install diffusers transformers scipy torch
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = ""DGSpitzer/Cyberpunk-Anime-Diffusion""
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to(""cuda"")
+
+prompt = ""a beautiful perfect face girl in dgs illustration style, Anime fine details portrait of school girl in front of modern tokyo city landscape on the background deep bokeh, anime masterpiece, 8k, sharp high quality anime""
+image = pipe(prompt).images[0]
+
+image.save(""./cyberpunk_girl.png"")
+```
+
+# Online Demo
+
+You can try the Online Web UI demo build with [Gradio](https://github.com/gradio-app/gradio), or use Colab Notebook at here:
+
+*My Online Space Demo*
+[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/DGSpitzer/DGS-Diffusion-Space)
+
+*Finetuned Diffusion WebUI Demo by anzorq*
+[![Use Finetuned_Diffusion WebUI](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/anzorq/finetuned_diffusion)
+
+*Colab Notebook*
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HelixNGC7293/cyberpunk-anime-diffusion/blob/main/cyberpunk_anime_diffusion.ipynb)[![GitHub](https://badgen.net/badge/icon/Github?icon=github&label)](https://github.com/HelixNGC7293/cyberpunk-anime-diffusion)
+
+*Buy me a coffee if you like this project ;P ♥*
+[![Buy me a coffee](https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/dgspitzer)
+
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/1.jpg"" width=""512"" height=""512""/></center>
+
+# **👇Model👇**
+
+AI Model Weights available at huggingface: https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion
+
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/2.jpg"" width=""512"" height=""512""/></center>
+
+# Usage
+
+After model loaded, use keyword **dgs** in your prompt, with **illustration style** to get even better results.
+
+For sampler, use **Euler A** for the best result (**DDIM** kinda works too), CFG Scale 7, steps 20 should be fine
+
+**Example 1:**
+
+```
+portrait of a girl in dgs illustration style, Anime girl, female soldier working in a cyberpunk city, cleavage, ((perfect femine face)), intricate, 8k, highly detailed, shy, digital painting, intense, sharp focus
+```
+
+For cyber robot male character, you can add **muscular male** to improve the output.
+
+**Example 2:**
+
+```
+a photo of muscular beard soldier male in dgs illustration style, half-body, holding robot arms, strong chest
+```
+
+**Example 3 (with Stable Diffusion WebUI):**
+
+If using [AUTOMATIC1111's Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+
+You can simply use this as **prompt** with **Euler A** Sampler,  CFG Scale 7, steps 20, 704 x 704px output res:
+
+```
+an anime girl in dgs illustration style
+```
+
+And set the **negative prompt** as this to get cleaner face: 
+
+```
+out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers
+```
+
+This will give you the exactly same style as the sample images above.
+
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/ReadmeAddon.jpg"" width=""256"" height=""353""/></center>
+
+---
+
+**NOTE: usage of this model implies accpetance of stable diffusion's [CreativeML Open RAIL-M license](LICENSE)**
+
+---
+
+
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/4.jpg"" width=""700"" height=""700""/></center>
+
+<center><img src=""https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/6.jpg"" width=""700"" height=""700""/></center>
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""Evel/Evel_Space"", ""luisrguerra/sd-real-dream-lcm-cpu"", ""Daniela-C/6x_Image_diffusion"", ""akhaliq/webui-orangemixs"", ""Dao3/Text-To-image-AllModels"", ""phenixrhyder/PrintingPress"", ""John6666/hfd_test_nostopbutton"", ""ConceptArtHouse/webui-gameasset"", ""mindtube/Diffusion50XX"", ""TheKitten/Fast-Images-Creature"", ""Nymbo/Diffusion80XX4sg"", ""YeOldHermit/StableDiffusion_AnythingV3_ModelCamenduru"", ""zwv9/webui-cpu"", ""kaleidoskop-hug/PrintingPress"", ""Adam111/stable-diffusion-webui"", ""vs4vijay/stable-diffusion"", ""Yasu55/stable-diffusion-webui"", ""ennov8ion/stablediffusion-models"", ""Shocky/Pink-Anime"", ""ReiPlush64/finetuned_diffusion"", ""John6666/ToyWorld4"", ""sasaro/webui"", ""Omnibus-archive/Diffusion-Flood"", ""Crossper6/stable-diffusion-webui"", ""grzegorz2047/fast_diffusion"", ""Alfasign/dIFFU"", ""Nymbo/PrintingPress"", ""Rifd/Sdallmodels"", ""John6666/Diffusion80XX4g"", ""NativeAngels/HuggingfaceDiffusion"", ""Malifex/CPU-Anything-V3.0-WebUI"", ""lianzhou/stable-diffusion-webui"", ""Missinginaction/stablediffusionwithnofilter"", ""arthurdias/Webui-Cpu-ExtensionV2-Publictest-WithCivitaiHelper"", ""thestasi/Webui-Cpu-ExtensionV2-Publictest-WithCivitaiHelper"", ""achyuth1344/stable-diffusion-webui"", ""ennov8ion/Scifi-Models"", ""ennov8ion/semirealistic-models"", ""Jackflack09/finetuned_diffusion2"", ""ennov8ion/dreamlike-models"", ""ennov8ion/FantasyArt-Models"", ""noes14155/img_All_models"", ""ennov8ion/500models"", ""Minecraft3193092/Stable-Diffusion-8"", ""AnimeStudio/anime-models"", ""John6666/Diffusion80XX4"", ""K00B404/HuggingfaceDiffusion_custom"", ""John6666/blitz_diffusion4"", ""John6666/blitz_diffusion_builtin"", ""deaf1296/finetuned_diffusion"", ""pieeetre/stable-diffusion-webui"", ""luluneko1/stable-diffusion-webui"", ""Lyra121/finetuned_diffusion"", ""voltcutter/stable-diffusion-webui"", ""hylee/finetuned_diffusion"", ""RhythmRemix14/PrintingPressDx"", ""Minecraft3193092/Stable-Diffusion-7"", ""sohoso/PrintingPress"", ""NativeAngels/ToyWorld"", ""AiiluoChen/webui"", ""Heckeroo/Cyberpunk-Anime-Diffusion"", ""Eduger/webui"", ""bobathetheft/webui"", ""natvill/stable-diffusion-webui"", ""Danielito/webui"", ""Eyeszik/webui"", ""YuraM/Stable-Diffusion-Protogen-webui"", ""TheFellow42/webui"", ""OswaldDev/webuih"", ""trhacknon/webui"", ""Harshveer/Finetuned_Diffusion_Max"", ""gato001k1/maximum_diffusion0k"", ""rubberboy/stable-diffusion-webui"", ""hilmyblaze/WebUI-Counterfeit-V2.5""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-06-21 20:44:20+00:00"", ""cardData"": ""language:\n- en\nlicense: creativeml-openrail-m\ntags:\n- cyberpunk\n- anime\n- waifu-diffusion\n- stable-diffusion\n- aiart\n- text-to-image\nthumbnail: https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/thumbnail.png"", ""transformersInfo"": null, ""_id"": ""635ab9b93180c590f4f48db9"", ""modelId"": ""DGSpitzer/Cyberpunk-Anime-Diffusion"", ""usedStorage"": 24647194668}",0,,0,,0,,0,,0,"DGSpitzer/DGS-Diffusion-Space, IAmXenos21/stable-diffusion-webui-VORST2, Joeythemonster/Text-To-image-AllModels, John6666/Diffusion80XX4sg, John6666/PrintingPress4, Nymbo/image_gen_supaqueue, PeepDaSlan9/B2BMGMT_Diffusion60XX, Yntec/PrintingPress, Yntec/ToyWorld, Yntec/blitz_diffusion, anzorq/finetuned_diffusion, darkstorm2150/Stable-Diffusion-Protogen-x3.4-webui, huggingface/InferenceSupport/discussions/new?title=DGSpitzer/Cyberpunk-Anime-Diffusion&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BDGSpitzer%2FCyberpunk-Anime-Diffusion%5D(%2FDGSpitzer%2FCyberpunk-Anime-Diffusion)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, phenixrhyder/NSFW-ToyWorld, vorstcavry/ai",15

DeepSeek-R1-Distill-Llama-8B-GGUF_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,599 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF,"---
+base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+language:
+- en
+library_name: transformers
+license: llama3.1
+tags:
+- deepseek
+- unsloth
+- transformers
+- llama
+- llama-3
+- meta
+---
+<div>
+  <p style=""margin-bottom: 0; margin-top: 0;"">
+    <strong>See <a href=""https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5"">our collection</a> for versions of Deepseek-R1 including GGUF & 4-bit formats.</strong>
+  </p>
+  <p style=""margin-bottom: 0;"">
+    <em>Unsloth's DeepSeek-R1 <a href=""https://unsloth.ai/blog/deepseekr1-dynamic"">1.58-bit + 2-bit Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 1-bit/2-bit.</em>
+  </p>
+  <div style=""display: flex; gap: 5px; align-items: center; "">
+    <a href=""https://github.com/unslothai/unsloth/"">
+      <img src=""https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png"" width=""133"">
+    </a>
+    <a href=""https://discord.gg/unsloth"">
+      <img src=""https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png"" width=""173"">
+    </a>
+    <a href=""https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device"">
+      <img src=""https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png"" width=""143"">
+    </a>
+  </div>
+<h1 style=""margin-top: 0rem;"">Instructions to run this model in llama.cpp:</h2>
+</div>
+
+You can view more detailed instructions in our blog: [unsloth.ai/blog/deepseek-r1](https://unsloth.ai/blog/deepseek-r1)
+1. Do not forget about `<｜User｜>` and `<｜Assistant｜>` tokens! - Or use a chat template formatter
+2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp
+3. Example with Q8_0 K quantized cache **Notice -no-cnv disables auto conversation mode**
+   ```bash
+   ./llama.cpp/llama-cli \
+       --model unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF/DeepSeek-R1-Distill-Llama-8B-Q4_K_M.gguf \
+       --cache-type-k q8_0 \
+       --threads 16 \
+       --prompt '<｜User｜>What is 1+1?<｜Assistant｜>' \
+       -no-cnv
+   ```
+   Example output:
+   
+   ```txt
+    <think>
+    Okay, so I need to figure out what 1 plus 1 is. Hmm, where do I even start? I remember from school that adding numbers is pretty basic, but I want to make sure I understand it properly.
+    Let me think, 1 plus 1. So, I have one item and I add another one. Maybe like a apple plus another apple. If I have one apple and someone gives me another, I now have two apples. So, 1 plus 1 should be 2. That makes sense.
+    Wait, but sometimes math can be tricky. Could it be something else? Like, in a different number system maybe? But I think the question is straightforward, using regular numbers, not like binary or hexadecimal or anything.
+    I also recall that in arithmetic, addition is combining quantities. So, if you have two quantities of 1, combining them gives you a total of 2. Yeah, that seems right.
+    Is there a scenario where 1 plus 1 wouldn't be 2? I can't think of any...
+   ```
+   
+4. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers.
+   ```bash
+   ./llama.cpp/llama-cli \
+   --model unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF/DeepSeek-R1-Distill-Llama-8B-Q4_K_M.gguf
+   --cache-type-k q8_0 
+   --threads 16 
+   --prompt '<｜User｜>What is 1+1?<｜Assistant｜>'
+   --n-gpu-layers 20 \
+    -no-cnv
+   ```
+   
+# Finetune your own Reasoning model like R1 with Unsloth!
+We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb
+
+[<img src=""https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png"" width=""200""/>](https://discord.gg/unsloth)
+[<img src=""https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png"" width=""200""/>](https://github.com/unslothai/unsloth)
+
+
+## ✨ Finetune for Free
+
+All notebooks are **beginner friendly**! Add your dataset, click ""Run All"", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
+
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **GRPO with Phi-4 (14B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb)               | 2x faster | 80% less |
+| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2.4x faster | 58% less |
+| **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)               | 2x faster | 60% less |
+| **Qwen2 VL (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb)               | 1.8x faster | 60% less |
+| **Qwen2.5 (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb)               | 2x faster | 60% less |
+| **Llama-3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 2.4x faster | 58% less |
+| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb)               | 2x faster | 50% less |
+| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb)               | 2.4x faster | 58% less |
+| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb)               | 2.2x faster | 62% less |
+
+[<img src=""https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png"" width=""200""/>](https://docs.unsloth.ai)
+
+- This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates.
+- This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
+- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
+
+## Special Thanks
+A huge thank you to the DeepSeek team for creating and releasing these models.
+
+
+# DeepSeek-R1
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align=""center"">
+  <img src=""https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true"" width=""60%"" alt=""DeepSeek-V3"" />
+</div>
+<hr>
+<div align=""center"" style=""line-height: 1;"">
+  <a href=""https://www.deepseek.com/"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Homepage"" src=""https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+  <a href=""https://chat.deepseek.com/"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Chat"" src=""https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+  <a href=""https://huggingface.co/deepseek-ai"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Hugging Face"" src=""https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+</div>
+
+<div align=""center"" style=""line-height: 1;"">
+  <a href=""https://discord.gg/Tc7c45Zzu5"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Discord"" src=""https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+  <a href=""https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Wechat"" src=""https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+  <a href=""https://twitter.com/deepseek_ai"" target=""_blank"" style=""margin: 2px;"">
+    <img alt=""Twitter Follow"" src=""https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+</div>
+
+<div align=""center"" style=""line-height: 1;"">
+  <a href=""https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-CODE"" style=""margin: 2px;"">
+    <img alt=""Code License"" src=""https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+  <a href=""https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-MODEL"" style=""margin: 2px;"">
+    <img alt=""Model License"" src=""https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53"" style=""display: inline-block; vertical-align: middle;""/>
+  </a>
+</div>
+
+
+<p align=""center"">
+  <a href=""https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf""><b>Paper Link</b>👁️</a>
+</p>
+
+
+## 1. Introduction
+
+We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. 
+DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
+With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
+However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
+we introduce DeepSeek-R1, which incorporates cold-start data before RL.
+DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. 
+To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
+
+**NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section.**
+
+<p align=""center"">
+  <img width=""80%"" src=""figures/benchmark.jpg"">
+</p>
+
+## 2. Model Summary
+
+---
+
+**Post-Training: Large-Scale Reinforcement Learning on the Base Model**
+
+-  We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
+
+-   We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
+    We believe the pipeline will benefit the industry by creating better models. 
+
+---
+
+**Distillation: Smaller Models Can Be Powerful Too**
+
+-  We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. 
+- Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
+
+## 3. Model Downloads
+
+### DeepSeek-R1 Models
+
+<div align=""center"">
+
+| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
+| :------------: | :------------: | :------------: | :------------: | :------------: |
+| DeepSeek-R1-Zero | 671B | 37B | 128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero)   |
+| DeepSeek-R1   | 671B | 37B |  128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1)   |
+
+</div>
+
+DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. 
+For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository.
+
+### DeepSeek-R1-Distill Models
+
+<div align=""center"">
+
+| **Model** | **Base Model** | **Download** |
+| :------------: | :------------: | :------------: |
+| DeepSeek-R1-Distill-Qwen-1.5B  | [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)   |
+| DeepSeek-R1-Distill-Qwen-7B  | [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)   |
+| DeepSeek-R1-Distill-Llama-8B  | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)   |
+| DeepSeek-R1-Distill-Qwen-14B   | [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)   |
+|DeepSeek-R1-Distill-Qwen-32B  | [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)   |
+| DeepSeek-R1-Distill-Llama-70B  | [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B)   |
+
+</div>
+
+DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
+We slightly change their configs and tokenizers. Please use our setting to run these models.
+
+## 4. Evaluation Results
+
+### DeepSeek-R1-Evaluation
+ For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1.
+<div align=""center"">
+
+
+| Category | Benchmark (Metric) | Claude-3.5-Sonnet-1022 | GPT-4o 0513 | DeepSeek V3 | OpenAI o1-mini | OpenAI o1-1217 | DeepSeek R1 |
+|----------|-------------------|----------------------|------------|--------------|----------------|------------|--------------|
+| | Architecture | - | - | MoE | - | - | MoE |
+| | # Activated Params | - | - | 37B | - | - | 37B |
+| | # Total Params | - | - | 671B | - | - | 671B |
+| English | MMLU (Pass@1) | 88.3 | 87.2 | 88.5 | 85.2 | **91.8** | 90.8 |
+| | MMLU-Redux (EM) | 88.9 | 88.0 | 89.1 | 86.7 | - | **92.9** |
+| | MMLU-Pro (EM) | 78.0 | 72.6 | 75.9 | 80.3 | - | **84.0** |
+| | DROP (3-shot F1) | 88.3 | 83.7 | 91.6 | 83.9 | 90.2 | **92.2** |
+| | IF-Eval (Prompt Strict) | **86.5** | 84.3 | 86.1 | 84.8 | - | 83.3 |
+| | GPQA-Diamond (Pass@1) | 65.0 | 49.9 | 59.1 | 60.0 | **75.7** | 71.5 |
+| | SimpleQA (Correct) | 28.4 | 38.2 | 24.9 | 7.0 | **47.0** | 30.1 |
+| | FRAMES (Acc.) | 72.5 | 80.5 | 73.3 | 76.9 | - | **82.5** |
+| | AlpacaEval2.0 (LC-winrate) | 52.0 | 51.1 | 70.0 | 57.8 | - | **87.6** |
+| | ArenaHard (GPT-4-1106) | 85.2 | 80.4 | 85.5 | 92.0 | - | **92.3** |
+| Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | **65.9** |
+| | Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | **96.6** | 96.3 |
+| | Codeforces (Rating) | 717 | 759 | 1134 | 1820 | **2061** | 2029 |
+| | SWE Verified (Resolved) | **50.8** | 38.8 | 42.0 | 41.6 | 48.9 | 49.2 |
+| | Aider-Polyglot (Acc.) | 45.3 | 16.0 | 49.6 | 32.9 | **61.7** | 53.3 |
+| Math | AIME 2024 (Pass@1) | 16.0 | 9.3 | 39.2 | 63.6 | 79.2 | **79.8** |
+| | MATH-500 (Pass@1) | 78.3 | 74.6 | 90.2 | 90.0 | 96.4 | **97.3** |
+| | CNMO 2024 (Pass@1) | 13.1 | 10.8 | 43.2 | 67.6 | - | **78.8** |
+| Chinese | CLUEWSC (EM) | 85.4 | 87.9 | 90.9 | 89.9 | - | **92.8** |
+| | C-Eval (EM) | 76.7 | 76.0 | 86.5 | 68.9 | - | **91.8** |
+| | C-SimpleQA (Correct) | 55.4 | 58.7 | **68.0** | 40.3 | - | 63.7 |
+
+</div>
+
+
+### Distilled Model Evaluation
+
+
+<div align=""center"">
+
+| Model                                    | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
+|------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
+| GPT-4o-0513                          | 9.3              | 13.4              | 74.6            | 49.9                 | 32.9                 | 759               |
+| Claude-3.5-Sonnet-1022             | 16.0             | 26.7                 | 78.3            | 65.0                 | 38.9                 | 717               |
+| o1-mini                              | 63.6             | 80.0              | 90.0            | 60.0                 | 53.8                 | **1820**          |
+| QwQ-32B-Preview                              | 44.0             | 60.0                 | 90.6            | 54.5               | 41.9                 | 1316              |
+| DeepSeek-R1-Distill-Qwen-1.5B       | 28.9             | 52.7              | 83.9            | 33.8                 | 16.9                 | 954               |
+| DeepSeek-R1-Distill-Qwen-7B          | 55.5             | 83.3              | 92.8            | 49.1                 | 37.6                 | 1189              |
+| DeepSeek-R1-Distill-Qwen-14B         | 69.7             | 80.0              | 93.9            | 59.1                 | 53.1                 | 1481              |
+| DeepSeek-R1-Distill-Qwen-32B        | **72.6**         | 83.3              | 94.3            | 62.1                 | 57.2                 | 1691              |
+| DeepSeek-R1-Distill-Llama-8B         | 50.4             | 80.0              | 89.1            | 49.0                 | 39.6                 | 1205              |
+| DeepSeek-R1-Distill-Llama-70B        | 70.0             | **86.7**          | **94.5**        | **65.2**             | **57.5**             | 1633              |
+
+</div>
+
+
+## 5. Chat Website & API Platform
+You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button ""DeepThink""
+
+We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
+
+## 6. How to Run Locally
+
+### DeepSeek-R1 Models
+
+Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally.
+
+### DeepSeek-R1-Distill Models
+
+DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models.
+
+For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm):
+
+```shell
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager
+```
+
+You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang)
+
+```bash
+python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2
+```
+
+### Usage Recommendations
+
+**We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:**
+
+1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
+2. **Avoid adding a system prompt; all instructions should be contained within the user prompt.**
+3. For mathematical problems, it is advisable to include a directive in your prompt such as: ""Please reason step by step, and put your final answer within \boxed{}.""
+4. When evaluating model performance, it is recommended to conduct multiple tests and average the results.
+
+## 7. License
+This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE).
+DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:
+- DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1.
+- DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE).
+- DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE).
+
+## 8. Citation
+```
+@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
+      title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, 
+      author={DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang},
+      year={2025},
+      eprint={2501.12948},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.12948}, 
+}
+
+```
+
+## 9. Contact
+If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
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+pohsjxx/u-vertex-r1,"---
+library_name: transformers
+tags:
+- unsloth
+license: apache-2.0
+datasets:
+- pohsjxx/drone-logistics-cot-dataset
+language:
+- zh
+base_model:
+- unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF
+pipeline_tag: question-answering
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]","{""id"": ""pohsjxx/u-vertex-r1"", ""author"": ""pohsjxx"", ""sha"": ""375ef9334e88c7a1a1398ddfdcbffa84722fb6be"", ""last_modified"": ""2025-02-05 09:16:31+00:00"", ""created_at"": ""2025-02-05 03:47:03+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 5, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""unsloth"", ""question-answering"", ""zh"", ""dataset:pohsjxx/drone-logistics-cot-dataset"", ""arxiv:1910.09700"", ""base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF"", ""base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""question-answering"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF\ndatasets:\n- pohsjxx/drone-logistics-cot-dataset\nlanguage:\n- zh\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: question-answering\ntags:\n- unsloth"", ""widget_data"": [{""text"": ""\u6211\u4f4f\u5728\u54ea\u91cc\uff1f"", ""context"": ""\u6211\u53eb\u6c83\u5c14\u592b\u5188\uff0c\u6211\u4f4f\u5728\u67cf\u6797\u3002""}, {""text"": ""\u6211\u4f4f\u5728\u54ea\u91cc\uff1f"", ""context"": ""\u6211\u53eb\u8428\u62c9\uff0c\u6211\u4f4f\u5728\u4f26\u6566\u3002""}, {""text"": ""\u6211\u7684\u540d\u5b57\u662f\u4ec0\u4e48\uff1f"", ""context"": ""\u6211\u53eb\u514b\u62c9\u62c9\uff0c\u6211\u4f4f\u5728\u4f2f\u514b\u5229\u3002""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""chat_template"": ""{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<\uff5cUser\uff5c>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<\uff5cAssistant\uff5c><\uff5ctool\u2581calls\u2581begin\uff5c><\uff5ctool\u2581call\u2581begin\uff5c>' + tool['type'] + '<\uff5ctool\u2581sep\uff5c>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<\uff5ctool\u2581call\u2581end\uff5c>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<\uff5ctool\u2581call\u2581begin\uff5c>' + tool['type'] + '<\uff5ctool\u2581sep\uff5c>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<\uff5ctool\u2581call\u2581end\uff5c>'}}{{'<\uff5ctool\u2581calls\u2581end\uff5c><\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<\uff5ctool\u2581outputs\u2581end\uff5c>' + message['content'] + '<\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<\uff5cAssistant\uff5c>' + content + '<\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<\uff5ctool\u2581outputs\u2581begin\uff5c><\uff5ctool\u2581output\u2581begin\uff5c>' + message['content'] + '<\uff5ctool\u2581output\u2581end\uff5c>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<\uff5ctool\u2581output\u2581begin\uff5c>' + message['content'] + '<\uff5ctool\u2581output\u2581end\uff5c>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<\uff5ctool\u2581outputs\u2581end\uff5c>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<\uff5cAssistant\uff5c>'}}{% endif %}"", ""eos_token"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""pad_token"": ""<|finetune_right_pad_id|>"", ""unk_token"": null, ""use_default_system_prompt"": false}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""F16"": 8030261248}, ""total"": 8030261248}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-02-05 09:16:31+00:00"", ""cardData"": ""base_model:\n- unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF\ndatasets:\n- pohsjxx/drone-logistics-cot-dataset\nlanguage:\n- zh\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: question-answering\ntags:\n- unsloth"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""67a2df376de2b0856429e9c0"", ""modelId"": ""pohsjxx/u-vertex-r1"", ""usedStorage"": 16245597866}",1,,0,,0,https://huggingface.co/mradermacher/u-vertex-r1-GGUF,1,,0,huggingface/InferenceSupport/discussions/new?title=pohsjxx/u-vertex-r1&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bpohsjxx%2Fu-vertex-r1%5D(%2Fpohsjxx%2Fu-vertex-r1)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+Paula139/DeepSeek-R1-destill-llama3-8b-arabic-fine-tuned,"---
+license: apache-2.0
+datasets:
+- maanasharma5/arabic_sft_data
+language:
+- ar
+- en
+base_model:
+- unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF
+pipeline_tag: question-answering
+tags:
+- unsloth
+- trl
+- sft
+---
+# Arabic-Reasoning-LLM: Fine-Tuning DeepSeek-R1-Llama3-8B for Advanced Arabic Reasoning
+
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Hugging%20Face-yellow)](https://huggingface.co/Paula139/DeepSeek-R1-destill-llama3-8b-arabic-fine-tuned)
+[![Kaggle](https://img.shields.io/badge/Kaggle-035a7d?logo=kaggle&logoColor=white)](https://www.kaggle.com/code/paulaadel/deepseek-r1-distill-llama-3/edit)
+https://wandb.ai/pakks/Fine-tune-DeepSeek-R1-Distill-Llama-8B%20on%20Medical%20COT%20Dataset/reports/Fine-tuning-Deepseek-r1-distill-llama3-8b-on-arabic-dataset--VmlldzoxMjAxMDEzOQ
+
+**Arabic-Reasoning-LLM** is a specialized language model optimized for advanced reasoning tasks in Arabic, built through efficient fine-tuning of the DeepSeek-R1-Llama3-8B architecture using state-of-the-art optimization techniques and curated Arabic datasets.
+
+## Overview
+
+This project addresses the critical need for high-performance Arabic reasoning models by implementing:
+- **Domain-Specific Fine-Tuning**: Leveraging carefully curated Arabic datasets spanning logical reasoning, mathematical problem-solving, and cultural context understanding
+- **Optimized Training Pipeline**: Utilizing Unsloth's memory-efficient framework and DeepSeek's R1 distillation techniques
+- **Cultural & Linguistic Adaptation**: Specialized tokenization and alignment for Arabic syntax and semantic structures
+
+## Key Features
+
+- 🚀 **4x Faster Training** with Unsloth's memory-optimized LoRA implementation
+- 🖥️ **Kaggle-Ready** with full GPU-accelerated notebook support
+- 📈 **23% Improved Accuracy** on Arabic reasoning benchmarks compared to base model
+- 🎯 **Task-Specific Adaptation** for:
+  - Logical deduction
+  - Cultural context understanding
+  - Multi-step Arabic textual reasoning
+- 🌍 **Full Arabic Script Support** with extended tokenizer vocabulary
+- 📦 **Hugging Face Integration** for seamless deployment
+
+## Model Architecture
+
+```mermaid
+graph TD
+    A[Base Model: DeepSeek-R1-Llama3-8B] --> B[Arabic Dataset Curation]
+    B --> C[Unsloth Optimization Layer]
+    C --> D[Adaptive LoRA Fine-Tuning]
+    D --> E[Cultural Context Alignment]
+    E --> F[Arabic-Reasoning-LLM]
+","{""id"": ""Paula139/DeepSeek-R1-destill-llama3-8b-arabic-fine-tuned"", ""author"": ""Paula139"", ""sha"": ""aa307dd043f61813349fc239ed2f621e919bea9a"", ""last_modified"": ""2025-03-31 15:49:52+00:00"", ""created_at"": ""2025-03-27 16:17:23+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 7, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""pytorch"", ""safetensors"", ""llama"", ""unsloth"", ""trl"", ""sft"", ""question-answering"", ""ar"", ""en"", ""dataset:maanasharma5/arabic_sft_data"", ""base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF"", ""base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": ""question-answering"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF\ndatasets:\n- maanasharma5/arabic_sft_data\nlanguage:\n- ar\n- en\nlicense: apache-2.0\npipeline_tag: question-answering\ntags:\n- unsloth\n- trl\n- sft"", ""widget_data"": [{""text"": ""\u0623\u064a\u0646 \u0623\u0633\u0643\u0646\u061f"", ""context"": ""\u0625\u0633\u0645\u064a \u0645\u062d\u0645\u062f \u0648\u0623\u0633\u0643\u0646 \u0641\u064a \u0628\u064a\u0631\u0648\u062a""}, {""text"": ""\u0623\u064a\u0646 \u0623\u0633\u0643\u0646\u061f"", ""context"": ""\u0625\u0633\u0645\u064a \u0633\u0627\u0631\u0647 \u0648\u0623\u0633\u0643\u0646 \u0641\u064a \u0644\u0646\u062f\u0646""}, {""text"": ""\u0645\u0627 \u0627\u0633\u0645\u064a\u061f"", ""context"": ""\u0627\u0633\u0645\u064a \u0633\u0639\u064a\u062f \u0648\u0623\u0633\u0643\u0646 \u0641\u064a \u062d\u064a\u0641\u0627.""}, {""text"": ""\u0645\u0627 \u0644\u0642\u0628 \u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0628\u0627\u0644\u0639\u0631\u0628\u064a\u0629\u061f"", ""context"": ""\u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0645\u0646 \u0623\u0628\u0637\u0627\u0644 \u0648\u0642\u0627\u062f\u0629 \u0627\u0644\u0641\u062a\u062d \u0627\u0644\u0625\u0633\u0644\u0627\u0645\u064a \u0648\u0642\u062f \u062a\u062d\u062f\u062b\u062a \u0639\u0646\u0647 \u0627\u0644\u0644\u063a\u0627\u062a \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a\u0629 \u0648\u0627\u0644\u0641\u0631\u0646\u0633\u064a\u0629 \u0648\u0627\u0644\u0625\u0633\u0628\u0627\u0646\u064a\u0629 \u0648\u0644\u0642\u0628 \u0628\u0633\u064a\u0641 \u0627\u0644\u0644\u0647 \u0627\u0644\u0645\u0633\u0644\u0648\u0644.""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""chat_template"": ""{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, 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'\\n' + tool['function']['arguments'] + '\\n' + '```' + '<\uff5ctool\u2581call\u2581end\uff5c>'}}{{'<\uff5ctool\u2581calls\u2581end\uff5c><\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<\uff5ctool\u2581outputs\u2581end\uff5c>' + message['content'] + '<\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<\uff5cAssistant\uff5c>' + content + '<\uff5cend\u2581of\u2581sentence\uff5c>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<\uff5ctool\u2581outputs\u2581begin\uff5c><\uff5ctool\u2581output\u2581begin\uff5c>' + message['content'] + '<\uff5ctool\u2581output\u2581end\uff5c>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<\uff5ctool\u2581output\u2581begin\uff5c>' + message['content'] + '<\uff5ctool\u2581output\u2581end\uff5c>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<\uff5ctool\u2581outputs\u2581end\uff5c>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<\uff5cAssistant\uff5c><think>\\n'}}{% endif %}"", ""eos_token"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""pad_token"": ""<|finetune_right_pad_id|>"", ""unk_token"": null, ""use_default_system_prompt"": false}}, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00004.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00004.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00003-of-00004.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00004-of-00004.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-03-31 15:49:52+00:00"", ""cardData"": 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Emu3-Gen_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,15 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+BAAI/Emu3-Gen,N/A,N/A,0,https://huggingface.co/lodrick-the-lafted/Emu3-Gen-12B,1,,0,,0,,0,"BAAI/Emu3, Nymbo/Emu3, akhaliq/emu3, eduagarcia/open_pt_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=BAAI/Emu3-Gen&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BBAAI%2FEmu3-Gen%5D(%2FBAAI%2FEmu3-Gen)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A",5
+lodrick-the-lafted/Emu3-Gen-12B,"---
+license: apache-2.0
+base_model:
+- BAAI/Emu3-Gen
+library_name: transformers
+tags:
+- merge
+---
+
+This is an interpolated upscale of [BAAI/Emu3-Gen](https://huggingface.co/BAAI/Emu3-Gen) from 8B to 11.5B.
+For each layer in [7,8,9,10,11,12,13,14,15,16,22,23,24], the weights were lerp'd between the previous layer and the current and inserted between the two.
+
+Expansion script is [here](https://huggingface.co/lodrick-the-lafted/Emu3-Gen-12B/blob/main/emu3_expand.py).","{""id"": ""lodrick-the-lafted/Emu3-Gen-12B"", ""author"": ""lodrick-the-lafted"", ""sha"": ""024c1c9d5e88f545d92c207b8826fb420a314d29"", ""last_modified"": ""2024-11-26 14:43:23+00:00"", ""created_at"": ""2024-09-30 14:27:56+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""Emu3"", ""text-generation"", ""merge"", ""custom_code"", ""base_model:BAAI/Emu3-Gen"", ""base_model:finetune:BAAI/Emu3-Gen"", ""license:apache-2.0"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- BAAI/Emu3-Gen\nlibrary_name: 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46182382808}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=lodrick-the-lafted/Emu3-Gen-12B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Blodrick-the-lafted%2FEmu3-Gen-12B%5D(%2Flodrick-the-lafted%2FEmu3-Gen-12B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

FastHunyuan_finetunes_20250426_221535.csv_finetunes_20250426_221535.csv

@@ -0,0 +1,60 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+FastVideo/FastHunyuan,"---
+pipeline_tag: text-to-video
+license: other
+license_name: tencent-hunyuan-community
+license_link: LICENSE
+---
+
+<p align=""center"">
+  <img src=""assets/logo.jpg""  height=30>
+</p>
+
+# FastHunyuan Model Card
+
+## Model Details
+
+FastHunyuan is an accelerated [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo) model. It can sample high quality videos with 6 diffusion steps. That brings around 8X speed up compared to the original HunyuanVideo with 50 steps.
+
+- **Developed by**: [Hao AI Lab](https://hao-ai-lab.github.io/)
+- **License**:  tencent-hunyuan-community
+- **Distilled from**: [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo)
+- **Github Repository**: https://github.com/hao-ai-lab/FastVideo
+
+## Usage
+
+- Clone [Fastvideo](https://github.com/hao-ai-lab/FastVideo) repository and follow the inference instructions in the README.
+- Alternatively, you can inference FastHunyuan using the official [Hunyuan Video repository](https://github.com/Tencent/HunyuanVideo) by  **setting the shift to 17 and steps to 6, resolution to 720X1280X125, and cfg bigger than 6**.
+We find that a large CFG scale generally leads to faster videos.
+
+## Training details
+
+FastHunyuan is consistency distillated on the [MixKit](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main) dataset with the following hyperparamters: 
+- Batch size: 16
+- Resulotion: 720x1280
+- Num of frames: 125
+- Train steps: 320
+- GPUs: 32
+- LR: 1e-6
+- Loss: huber
+
+## Evaluation
+We provide some qualitative comparison between FastHunyuan 6 step inference v.s. the original Hunyuan with 6 step inference: 
+
+| FastHunyuan 6 step | Hunyuan 6 step |
+| --- | --- |
+| ![FastHunyuan 6 step](assets/distilled/1.gif) | ![Hunyuan 6 step](assets/undistilled/1.gif) |
+| ![FastHunyuan 6 step](assets/distilled/2.gif) | ![Hunyuan 6 step](assets/undistilled/2.gif) |
+| ![FastHunyuan 6 step](assets/distilled/3.gif) | ![Hunyuan 6 step](assets/undistilled/3.gif) |
+| ![FastHunyuan 6 step](assets/distilled/4.gif) | ![Hunyuan 6 step](assets/undistilled/4.gif) |
+
+## Memory requirements
+
+Please check our github repo for details. https://github.com/hao-ai-lab/FastVideo
+
+For inference, we can inference FastHunyuan on single RTX4090. We now support NF4 and LLM-INT8 quantized inference using BitsAndBytes for FastHunyuan. With NF4 quantization, inference can be performed on a single RTX 4090 GPU, requiring just 20GB of VRAM.
+
+For Lora Finetune, minimum hardware requirement
+- 40 GB GPU memory each for 2 GPUs with lora
+- 30 GB GPU memory each for 2 GPUs with CPU offload and lora.
+","{""id"": ""FastVideo/FastHunyuan"", ""author"": ""FastVideo"", ""sha"": ""7113d8031b751b8734bdc08d60d2cbe27b16dca2"", ""last_modified"": ""2025-01-08 00:48:34+00:00"", ""created_at"": ""2024-12-16 00:27:41+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 97, ""downloads_all_time"": null, ""likes"": 186, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""safetensors"", ""text-to-video"", ""license:other"", ""region:us""], ""pipeline_tag"": ""text-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: other\nlicense_name: tencent-hunyuan-community\nlicense_link: LICENSE\npipeline_tag: text-to-video"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE', size=None, blob_id=None, lfs=None)"", 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""675f73fd9efed00f4da7ae05"", ""modelId"": ""FastVideo/FastHunyuan"", ""usedStorage"": 50190929002}",0,,0,,0,https://huggingface.co/city96/FastHunyuan-gguf,1,,0,huggingface/InferenceSupport/discussions/new?title=FastVideo/FastHunyuan&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BFastVideo%2FFastHunyuan%5D(%2FFastVideo%2FFastHunyuan)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

GPT-JT-6B-v1_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,159 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+togethercomputer/GPT-JT-6B-v1,"---
+datasets: 
+  - natural_instructions
+  - the_pile
+  - cot
+  - Muennighoff/P3
+inference: 
+  parameters: 
+    max_new_tokens: 5
+    temperature: 1.0
+    top_k: 1
+license: apache-2.0
+language: 
+  - en
+pipeline_tag: text-generation
+widget: 
+  - 
+    example_title: ""Sentiment Analysis""
+    text: |-
+        The task is to label the post's emotion as sadness, joy, love, anger, fear, or surprise.
+
+        Input: I'm feeling quite sad and sorry for myself but ill snap out of it soon.
+        Output: sadness
+        
+        Input: I am just feeling cranky and blue.
+        Output: anger
+        
+        Input: I can have for a treat or if i am feeling festive.
+        Output:
+  - 
+    example_title: ""Country Currency""
+    text: |-
+        Return the currency of the given country.
+        
+        Input: Switzerland
+        Output: Swiss Franc
+
+        Input: India
+        Output:
+  - 
+    example_title: ""Tweet Eval Hate""
+    text: |-
+        Label whether the following tweet contains hate speech against either immigrants or women. Hate Speech (HS) is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics.
+        Possible labels:
+        1. hate speech
+        2. not hate speech
+        
+        Tweet: HOW REFRESHING! In South Korea, there is no such thing as 'political correctness"" when it comes to dealing with Muslim refugee wannabes via @user
+        Label: hate speech
+        
+        Tweet: New to Twitter-- any men on here know what the process is to get #verified?
+        Label: not hate speech
+        
+        Tweet: Dont worry @user you are and will always be the most hysterical woman.
+        Label:
+  -
+    example_title: ""Entity Recognition""
+    text: |-
+        Extract all the names of people, places, and organizations from the following sentences.
+
+        Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last May.
+        Entities: Satya Nadella, Microsoft, Bahamas
+        
+        Sentence: Pacific Northwest cities include Seattle and Portland, which I have visited with Vikash.
+        Entities:
+  - 
+    example_title: ""Data Clearning""
+    text: |-
+        Format the data into a CSV file:
+
+        Input: Jane Doe jane.doe@gmail.com (520) 382 2435
+        Output: Jane Doe,jane.doe@gmail.com,520-382-2435
+        
+        Input: Peter Lee (510) 333-2429 email: peter@yahoo.com
+        Output:
+---
+
+<h1 style=""font-size: 42px"">GPT-JT<h1/>
+
+
+***<p style=""font-size: 24px"">Feel free to try out our [Online Demo](https://huggingface.co/spaces/togethercomputer/GPT-JT)!</p>***
+
+
+# Model Summary
+
+> With a new decentralized training algorithm, we fine-tuned GPT-J (6B) on 3.53 billion tokens, resulting in GPT-JT (6B), a model that outperforms many 100B+ parameter models on classification benchmarks.
+
+We incorporated a collection of open techniques and datasets to build GPT-JT:
+- GPT-JT is a fork of [EleutherAI](https://www.eleuther.ai)'s [GPT-J (6B)](https://huggingface.co/EleutherAI/gpt-j-6B);
+- We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, allowing the model to see bidirectional context of the prompt;
+- The model was trained on a large collection of diverse data, including [Chain-of-Thought (CoT)](https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html), [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3), [Natural-Instructions (NI) dataset](https://github.com/allenai/natural-instructions).
+
+With the help of techniques mentioned above, GPT-JT significantly improves the performance of classification tasks over the original GPT-J, and even outperforms most 100B+ parameter models!
+
+# Quick Start
+
+```python
+from transformers import pipeline
+pipe = pipeline(model='togethercomputer/GPT-JT-6B-v1')
+pipe('''""I love this!"" Is it positive? A:''')
+```
+or
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained(""togethercomputer/GPT-JT-6B-v1"")
+model = AutoModelForCausalLM.from_pretrained(""togethercomputer/GPT-JT-6B-v1"")
+```
+
+# License
+
+The weights of GPT-JT-6B-v1 are licensed under version 2.0 of the Apache License.
+
+# Training Details
+
+## UL2 Training Objective
+
+We train GPT-JT using UL2 training objective [1][2].
+The original GPT-J uses causal mask (as shown below left) for autoregressive generation. So for each token, it can only see its previous context.
+In order to fully leverage the context information, we continue to train GPT-J with UL2 training objectives, and uses causal mask with prefix (as shown below right) -- using bidirectional attention for the prompt / input and causal attention for token generation.
+Intuitively, being able to see context bidirectionally might improve downstream tasks that require this information.
+
+$$ 
+\begin{bmatrix}
+1 & 0 & 0 & 0 & 0 \\
+1 & 1 & 0 & 0 & 0 \\
+1 & 1 & 1 & 0 & 0 \\
+1 & 1 & 1 & 1 & 0 \\
+1 & 1 & 1 & 1 & 1 
+\end{bmatrix}
+
+\begin{bmatrix}
+1 & 1 & 1 & 0 & 0 \\
+1 & 1 & 1 & 0 & 0 \\
+1 & 1 & 1 & 0 & 0 \\
+1 & 1 & 1 & 1 & 0 \\
+1 & 1 & 1 & 1 & 1 
+\end{bmatrix}  
+$$
+
+Furthermore, we leverage a large collection of data, including [Natural-Instructions](https://github.com/allenai/natural-instructions), [P3](https://huggingface.co/datasets/Muennighoff/P3), [MMLU-COT](https://github.com/jasonwei20/flan-2/blob/main/mmlu-cot.json), and [the Pile](https://huggingface.co/datasets/the_pile)
+Specifically, we first conduct training for 2.62 billion tokens using the UL2 loss on the Pile, followed by 0.92 billion tokens with a mixture of the above datasets: 5% of COT, 20% of P3, 20% of NI, and 55% of the Pile.
+
+## Hyperparameters
+
+We used AdamW with a learning rate of 1e-5 and global batch size of 64 (16 for each data parallel worker).
+We used mix-precision training where the activation is in FP16 while the optimizer states are kept in FP32.
+We use both data parallelism and pipeline parallelism to conduct training.
+During training, we truncate the input sequence to 2048 tokens, and for input sequence that contains less than 2048 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.
+
+## Infrastructure
+
+We used [the Together Research Computer](https://together.xyz/) to conduct training. 
+
+# References
+
+[1]: Tay, Yi, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, and Donald Metzler. ""Unifying Language Learning Paradigms."" arXiv preprint arXiv:2205.05131 (2022).
+
+[2]: Tay, Yi, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia et al. ""Transcending scaling laws with 0.1% extra compute."" arXiv preprint arXiv:2210.11399 (2022).","{""id"": ""togethercomputer/GPT-JT-6B-v1"", ""author"": ""togethercomputer"", ""sha"": ""f34aa35f906895602c1f86f5685e598afdea8051"", ""last_modified"": ""2023-01-24 06:08:17+00:00"", ""created_at"": ""2022-11-24 06:09:34+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 4479, ""downloads_all_time"": null, ""likes"": 302, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""gptj"", ""text-generation"", ""en"", ""dataset:natural_instructions"", ""dataset:the_pile"", ""dataset:cot"", ""dataset:Muennighoff/P3"", ""arxiv:2205.05131"", ""arxiv:2210.11399"", ""license:apache-2.0"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""datasets:\n- natural_instructions\n- the_pile\n- cot\n- Muennighoff/P3\nlanguage:\n- en\nlicense: apache-2.0\npipeline_tag: text-generation\ninference:\n  parameters:\n    max_new_tokens: 5\n    temperature: 1\n    top_k: 1\nwidget:\n- example_title: Sentiment Analysis\n  text: 'The task is to label the post''s emotion as sadness, joy, love, anger, fear,\n    or surprise.\n\n\n    Input: I''m feeling quite sad and sorry for myself but ill snap out of it soon.\n\n    Output: sadness\n\n\n    Input: I am just feeling cranky and blue.\n\n    Output: anger\n\n\n    Input: I can have for a treat or if i am feeling festive.\n\n    Output:'\n- example_title: Country Currency\n  text: 'Return the currency of the given country.\n\n\n    Input: Switzerland\n\n    Output: Swiss Franc\n\n\n    Input: India\n\n    Output:'\n- example_title: Tweet Eval Hate\n  text: 'Label whether the following tweet contains hate speech against either immigrants\n    or women. Hate Speech (HS) is commonly defined as any communication that disparages\n    a person or a group on the basis of some characteristic such as race, color, ethnicity,\n    gender, sexual orientation, nationality, religion, or other characteristics.\n\n    Possible labels:\n\n    1. hate speech\n\n    2. not hate speech\n\n\n    Tweet: HOW REFRESHING! In South Korea, there is no such thing as ''political correctness\""\n    when it comes to dealing with Muslim refugee wannabes via @user\n\n    Label: hate speech\n\n\n    Tweet: New to Twitter-- any men on here know what the process is to get #verified?\n\n    Label: not hate speech\n\n\n    Tweet: Dont worry @user you are and will always be the most hysterical woman.\n\n    Label:'\n- example_title: Entity Recognition\n  text: 'Extract all the names of people, places, and organizations from the following\n    sentences.\n\n\n    Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last May.\n\n    Entities: Satya Nadella, Microsoft, Bahamas\n\n\n    Sentence: Pacific Northwest cities include Seattle and Portland, which I have\n    visited with Vikash.\n\n    Entities:'\n- example_title: Data Clearning\n  text: 'Format the data into a CSV file:\n\n\n    Input: Jane Doe jane.doe@gmail.com (520) 382 2435\n\n    Output: Jane Doe,jane.doe@gmail.com,520-382-2435\n\n\n    Input: Peter Lee (510) 333-2429 email: peter@yahoo.com\n\n    Output:'"", ""widget_data"": [{""example_title"": ""Sentiment Analysis"", ""text"": ""The task is to label the post's emotion as sadness, joy, love, anger, fear, or surprise.\n\nInput: I'm feeling quite sad and sorry for myself but ill snap out of it soon.\nOutput: sadness\n\nInput: I am just feeling cranky and blue.\nOutput: anger\n\nInput: I can have for a treat or if i am feeling festive.\nOutput:""}, {""example_title"": ""Country Currency"", ""text"": ""Return the currency of the given country.\n\nInput: Switzerland\nOutput: Swiss Franc\n\nInput: India\nOutput:""}, {""example_title"": ""Tweet Eval Hate"", ""text"": ""Label whether the following tweet contains hate speech against either immigrants or women. Hate Speech (HS) is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics.\nPossible labels:\n1. hate speech\n2. not hate speech\n\nTweet: HOW REFRESHING! In South Korea, there is no such thing as 'political correctness\"" when it comes to dealing with Muslim refugee wannabes via @user\nLabel: hate speech\n\nTweet: New to Twitter-- any men on here know what the process is to get #verified?\nLabel: not hate speech\n\nTweet: Dont worry @user you are and will always be the most hysterical woman.\nLabel:""}, {""example_title"": ""Entity Recognition"", ""text"": ""Extract all the names of people, places, and organizations from the following sentences.\n\nSentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last May.\nEntities: Satya Nadella, Microsoft, Bahamas\n\nSentence: Pacific Northwest cities include Seattle and Portland, which I have visited with Vikash.\nEntities:""}, {""example_title"": ""Data Clearning"", ""text"": ""Format the data into a CSV file:\n\nInput: Jane Doe jane.doe@gmail.com (520) 382 2435\nOutput: Jane Doe,jane.doe@gmail.com,520-382-2435\n\nInput: Peter Lee (510) 333-2429 email: peter@yahoo.com\nOutput:""}], ""model_index"": null, ""config"": {""architectures"": [""GPTJForCausalLM""], ""model_type"": ""gptj"", ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<|endoftext|>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""<|endoftext|>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""pad_token"": null, ""unk_token"": {""__type"": ""AddedToken"", ""content"": ""<|endoftext|>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""togethercomputer/GPT-JT"", ""Intel/low_bit_open_llm_leaderboard"", ""BAAI/open_cn_llm_leaderboard"", ""Sharathhebbar24/One-stop-for-Open-source-models"", ""gsaivinay/open_llm_leaderboard"", ""GTBench/GTBench"", ""Vikhrmodels/small-shlepa-lb"", ""kz-transformers/kaz-llm-lb"", ""felixz/open_llm_leaderboard"", ""OPTML-Group/UnlearnCanvas-Benchmark"", ""BAAI/open_flageval_vlm_leaderboard"", ""b1sheng/kg_llm_leaderboard_test"", ""neubla/neubla-llm-evaluation-board"", ""srikanthsrnvs/togethercomputer-GPT-JT-6B-v1"", ""rodrigomasini/data_only_open_llm_leaderboard"", ""Docfile/open_llm_leaderboard"", ""akhaliq/GPT-JT-6B-v1"", ""Emkay/Asrix_Bot"", ""jbest2007/PersonalFinanceAssistant"", ""clam004/demo-app"", ""smothiki/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard_two"", ""0x1668/open_llm_leaderboard"", ""pngwn/open_llm_leaderboard-check"", ""gorocdcdc/togethercomputer-GPT-JT-6B-v1"", ""asir0z/open_llm_leaderboard"", ""kbmlcoding/open_llm_leaderboard_free"", ""K00B404/One-stop-till-you-drop"", ""aichampions/open_llm_leaderboard"", ""Adeco/open_llm_leaderboard"", ""anirudh937/open_llm_leaderboard"", ""smothiki/open_llm_leaderboard2"", ""Asiya057/Incarna-Mind"", ""Asiya057/Incarna-Mind-POC"", ""mjalg/IFEvalTR"", ""creaturebot/GPT-JT""], ""safetensors"": null, 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the following tweet contains hate speech against either immigrants\n    or women. Hate Speech (HS) is commonly defined as any communication that disparages\n    a person or a group on the basis of some characteristic such as race, color, ethnicity,\n    gender, sexual orientation, nationality, religion, or other characteristics.\n\n    Possible labels:\n\n    1. hate speech\n\n    2. not hate speech\n\n\n    Tweet: HOW REFRESHING! In South Korea, there is no such thing as ''political correctness\""\n    when it comes to dealing with Muslim refugee wannabes via @user\n\n    Label: hate speech\n\n\n    Tweet: New to Twitter-- any men on here know what the process is to get #verified?\n\n    Label: not hate speech\n\n\n    Tweet: Dont worry @user you are and will always be the most hysterical woman.\n\n    Label:'\n- example_title: Entity Recognition\n  text: 'Extract all the names of people, places, and organizations from the following\n    sentences.\n\n\n    Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last May.\n\n    Entities: Satya Nadella, Microsoft, Bahamas\n\n\n    Sentence: Pacific Northwest cities include Seattle and Portland, which I have\n    visited with Vikash.\n\n    Entities:'\n- example_title: Data Clearning\n  text: 'Format the data into a CSV file:\n\n\n    Input: Jane Doe jane.doe@gmail.com (520) 382 2435\n\n    Output: Jane Doe,jane.doe@gmail.com,520-382-2435\n\n\n    Input: Peter Lee (510) 333-2429 email: peter@yahoo.com\n\n    Output:'"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""637f0a9e0bb954fb10e4837a"", ""modelId"": ""togethercomputer/GPT-JT-6B-v1"", ""usedStorage"": 24438556919}",0,,0,,0,,0,,0,"Asiya057/Incarna-Mind, BAAI/open_cn_llm_leaderboard, BAAI/open_flageval_vlm_leaderboard, GTBench/GTBench, Intel/low_bit_open_llm_leaderboard, OPTML-Group/UnlearnCanvas-Benchmark, Sharathhebbar24/One-stop-for-Open-source-models, Vikhrmodels/small-shlepa-lb, felixz/open_llm_leaderboard, gsaivinay/open_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=togethercomputer/GPT-JT-6B-v1&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Btogethercomputer%2FGPT-JT-6B-v1%5D(%2Ftogethercomputer%2FGPT-JT-6B-v1)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kz-transformers/kaz-llm-lb, togethercomputer/GPT-JT",13

Hotshot-XL_finetunes_20250426_121513.csv_finetunes_20250426_121513.csv

@@ -0,0 +1,48 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+hotshotco/Hotshot-XL,"---
+license: openrail++
+tags:
+- text-to-video
+- stable-diffusion
+---
+
+![image/gif](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/ux_sZKB9snVPsKRT1TzfG.gif)
+
+<font size=""32"">**Try Hotshot-XL yourself here**: https://www.hotshot.co</font>
+
+Hotshot-XL is an AI text-to-GIF model trained to work alongside [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
+
+Hotshot-XL can generate GIFs with any fine-tuned SDXL model. This means two things:
+  1. You’ll be able to make GIFs with any existing or newly fine-tuned SDXL model you may want to use.
+  2. If you'd like to make GIFs of personalized subjects, you can load your own SDXL based LORAs, and not have to worry about fine-tuning Hotshot-XL. This is awesome because it’s usually much easier to find suitable images for training data than it is to find videos. It also hopefully fits into everyone's existing LORA usage/workflows :) See more [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-personalized-loras).
+
+Hotshot-XL is compatible with SDXL ControlNet to make GIFs in the composition/layout you’d like. See [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-controlnet) for more info.
+
+Hotshot-XL was trained to generate 1 second GIFs at 8 FPS.
+
+Hotshot-XL was trained on various aspect ratios. For best results with the base Hotshot-XL model, we recommend using it with an SDXL model that has been fine-tuned with 512x512 images. You can find an SDXL model we fine-tuned for 512x512 resolutions [here](https://github.com/hotshotco/Hotshot-XL/blob/main/README.md#text-to-gif-with-personalized-loras).
+
+
+
+![image/gif](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/XXgnk14nIasPdkvkPlDzn.gif)
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/6OknWOlsl9Zs_esGtPTlZ.jpeg)
+
+Source code is available at https://github.com/hotshotco/Hotshot-XL.
+
+# Model Description
+- **Developed by**: Natural Synthetics Inc.
+- **Model type**: Diffusion-based text-to-GIF generative model
+- **License**: [CreativeML Open RAIL++-M License](https://huggingface.co/hotshotco/Hotshot-XL/raw/main/LICENSE.md)
+- **Model Description**: This is a model that can be used to generate and modify GIFs based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
+- **Resources for more information**: Check out our [GitHub Repository](https://github.com/hotshotco/Hotshot-XL).
+
+
+# Limitations and Bias
+## Limitations
+- The model does not achieve perfect photorealism
+- The model cannot render legible text
+- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
+- Faces and people in general may not be generated properly.
+
+## Bias
+While the capabilities of video generation models are impressive, they can also reinforce or exacerbate social biases.","{""id"": ""hotshotco/Hotshot-XL"", ""author"": ""hotshotco"", ""sha"": ""300d6a691ab6d62e74348f2e0d430e6d82ee2864"", ""last_modified"": ""2023-10-11 17:44:31+00:00"", ""created_at"": ""2023-10-03 08:20:30+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 2172, ""downloads_all_time"": null, ""likes"": 312, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""text-to-video"", ""stable-diffusion"", ""license:openrail++"", ""diffusers:HotshotXLPipeline"", ""region:us""], ""pipeline_tag"": ""text-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: openrail++\ntags:\n- text-to-video\n- stable-diffusion"", ""widget_data"": null, ""model_index"": null, ""config"": {""diffusers"": {""_class_name"": ""HotshotXLPipeline""}}, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hsxl_temporal_layers.f16.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hsxl_temporal_layers.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model_index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='scheduler/scheduler_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder_2/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder_2/model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/vocab.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/vocab.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='unet/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='unet/diffusion_pytorch_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/diffusion_pytorch_model.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [""TIGER-Lab/VideoScore-Leaderboard"", ""Rooni/TextToVideo"", ""7jimmy/imgtotext"", ""234bcn/gpt2"", ""crlizcan/test"", ""marselgames9/marselgames9-gif135"", ""marselgames9/marselgames9-gif135animation"", ""marselgames9/marselgames9-1234567gif"", ""soiz1/ComfyUI-Demo""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-10-11 17:44:31+00:00"", ""cardData"": ""license: openrail++\ntags:\n- text-to-video\n- stable-diffusion"", ""transformersInfo"": null, ""_id"": ""651bcecea2a4b126253ce4e1"", ""modelId"": ""hotshotco/Hotshot-XL"", ""usedStorage"": 16249041532}",0,,0,,0,,0,,0,"234bcn/gpt2, 7jimmy/imgtotext, Rooni/TextToVideo, TIGER-Lab/VideoScore-Leaderboard, crlizcan/test, huggingface/InferenceSupport/discussions/new?title=hotshotco/Hotshot-XL&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bhotshotco%2FHotshot-XL%5D(%2Fhotshotco%2FHotshot-XL)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, marselgames9/marselgames9-1234567gif, marselgames9/marselgames9-gif135, marselgames9/marselgames9-gif135animation, soiz1/ComfyUI-Demo",10

Hunyuan3D-1_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,258 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+tencent/Hunyuan3D-1,"---
+library_name: hunyuan3d-2
+license: other
+license_name: tencent-hunyuan-community
+license_link: https://huggingface.co/tencent/Hunyuan3D-1/blob/main/LICENSE.txt
+language:
+- en
+- zh
+tags:
+- image-to-3d
+- text-to-3d
+pipeline_tag: image-to-3d
+---
+<!-- ## **Hunyuan3D-1.0** -->
+
+<p align=""center"">
+  <img src=""./assets/logo.png""  height=200>
+</p>
+
+# Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation
+
+<div align=""center"">
+  <a href=""https://github.com/tencent/Hunyuan3D-1""><img src=""https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github-pages""></a> &ensp;
+  <a href=""https://3d.hunyuan.tencent.com""><img src=""https://img.shields.io/static/v1?label=Homepage&message=Tencent Hunyuan3D&color=blue&logo=github-pages""></a> &ensp;
+  <a href=""https://arxiv.org/pdf/2411.02293""><img src=""https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red&logo=arxiv""></a> &ensp;
+  <a href=""https://huggingface.co/Tencent/Hunyuan3D-1""><img src=""https://img.shields.io/static/v1?label=Checkpoints&message=HuggingFace&color=yellow""></a> &ensp;
+  <a href=""https://huggingface.co/spaces/Tencent/Hunyuan3D-1""><img src=""https://img.shields.io/static/v1?label=Demo&message=HuggingFace&color=yellow""></a> &ensp;
+</div>
+
+
+## 🔥🔥🔥 News!!
+
+* Nov 5, 2024: 💬 We support demo running  image_to_3d generation now. Please check the [script](#using-gradio) below.
+* Nov 5, 2024: 💬 We support demo running  text_to_3d generation now. Please check the [script](#using-gradio) below.
+
+
+## 📑 Open-source Plan
+
+- [x] Inference 
+- [x] Checkpoints
+- [ ] Baking related
+- [ ] Training
+- [ ] ComfyUI
+- [ ] Distillation Version
+- [ ] TensorRT Version
+
+
+
+## **Abstract**
+<p align=""center"">
+  <img src=""./assets/teaser.png""  height=450>
+</p>
+
+While 3D generative models have greatly improved artists' workflows, the existing diffusion models for 3D generation suffer from slow generation and poor generalization. To address this issue, we propose a two-stage approach named Hunyuan3D-1.0 including a lite version and a standard version, that both support text- and image-conditioned generation.
+
+In the first stage, we employ a multi-view diffusion model that efficiently generates multi-view RGB in approximately 4 seconds. These multi-view images capture rich details of the 3D asset from different viewpoints, relaxing the tasks from single-view to multi-view reconstruction. In the second stage, we introduce a feed-forward reconstruction model that rapidly and faithfully reconstructs the 3D asset given the generated multi-view images in approximately 7 seconds. The reconstruction network learns to handle noises and in-consistency introduced by the multi-view diffusion and leverages the available information from the condition image to efficiently recover the 3D structure.
+
+Our framework involves the text-to-image model, i.e., Hunyuan-DiT, making it a unified framework to support both text- and image-conditioned 3D generation. Our standard version has 3x more parameters than our lite and other existing model. Our Hunyuan3D-1.0 achieves an impressive balance between speed and quality, significantly reducing generation time while maintaining the quality and diversity of the produced assets.
+
+
+## 🎉 **Hunyuan3D-1 Architecture**
+
+<p align=""center"">
+  <img src=""./assets/overview_3.png""  height=400>
+</p>
+
+
+## 📈 Comparisons
+
+We have evaluated Hunyuan3D-1.0 with other open-source 3d-generation methods, our Hunyuan3D-1.0 received the highest user preference across 5 metrics. Details in the picture on the lower left.
+
+The lite model takes around 10 seconds to produce a 3D mesh from a single image on an NVIDIA A100 GPU, while the standard model takes roughly 25 seconds. The plot laid out in the lower right demonstrates that Hunyuan3D-1.0 achieves an optimal balance between quality and efficiency.
+
+<p align=""center"">
+  <img src=""./assets/radar.png""  height=300>
+  <img src=""./assets/runtime.png""  height=300>
+</p>
+
+## Get Started
+
+#### Begin by cloning the repository:
+
+```shell
+git clone https://github.com/tencent/Hunyuan3D-1
+cd Hunyuan3D-1
+```
+
+#### Installation Guide for Linux
+
+We provide an env_install.sh script file for setting up environment. 
+
+```
+# step 1, create conda env
+conda create -n hunyuan3d-1 python=3.9 or 3.10 or 3.11 or 3.12
+conda activate hunyuan3d-1
+
+# step 2. install torch realated package
+which pip # check pip corresponds to python
+
+# modify the cuda version according to your machine (recommended)
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+
+# step 3. install other packages
+bash env_install.sh
+```
+<details>
+<summary>💡Other tips for envrionment installation</summary>
+    
+Optionally, you can install xformers or flash_attn to acclerate computation:
+
+```
+pip install xformers --index-url https://download.pytorch.org/whl/cu121
+```
+```
+pip install flash_attn
+```
+
+Most environment errors are caused by a mismatch between machine and packages. You can try manually specifying the version, as shown in the following successful cases:
+```
+# python3.9
+pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118 
+```
+
+when install pytorch3d, the gcc version is preferably greater than 9, and the gpu driver should not be too old.
+
+</details>
+
+#### Download Pretrained Models
+
+The models are available at [https://huggingface.co/tencent/Hunyuan3D-1](https://huggingface.co/tencent/Hunyuan3D-1):
+
++ `Hunyuan3D-1/lite`, lite model for multi-view generation.
++ `Hunyuan3D-1/std`, standard model for multi-view generation.
++ `Hunyuan3D-1/svrm`, sparse-view reconstruction model.
+
+
+To download the model, first install the huggingface-cli. (Detailed instructions are available [here](https://huggingface.co/docs/huggingface_hub/guides/cli).)
+
+```shell
+python3 -m pip install ""huggingface_hub[cli]""
+```
+
+Then download the model using the following commands:
+
+```shell
+mkdir weights
+huggingface-cli download tencent/Hunyuan3D-1 --local-dir ./weights
+
+mkdir weights/hunyuanDiT
+huggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled --local-dir ./weights/hunyuanDiT
+```
+
+#### Inference 
+For text to 3d generation, we supports bilingual Chinese and English, you can use the following command to inference.
+```python
+python3 main.py \
+    --text_prompt ""a lovely rabbit"" \
+    --save_folder ./outputs/test/ \
+    --max_faces_num 90000 \
+    --do_texture_mapping \
+    --do_render
+```
+
+For image to 3d generation, you can use the following command to inference.
+```python
+python3 main.py \
+    --image_prompt ""/path/to/your/image"" \
+    --save_folder ./outputs/test/ \
+    --max_faces_num 90000 \
+    --do_texture_mapping \
+    --do_render
+```
+We list some more useful configurations for easy usage:
+
+|    Argument        |  Default  |                     Description                     |
+|:------------------:|:---------:|:---------------------------------------------------:|
+|`--text_prompt`  |   None    |The text prompt for 3D generation         |
+|`--image_prompt` |   None    |The image prompt for 3D generation         |
+|`--t2i_seed`     |    0      |The random seed for generating images        |
+|`--t2i_steps`    |    25     |The number of steps for sampling of text to image  |
+|`--gen_seed`     |    0      |The random seed for generating 3d generation        |
+|`--gen_steps`    |    50     |The number of steps for sampling of 3d generation  |
+|`--max_faces_numm` | 90000  |The limit number of faces of 3d mesh |
+|`--save_memory`   | False   |module will move to cpu automatically|
+|`--do_texture_mapping` |   False    |Change vertex shadding to texture shading  |
+|`--do_render`  |   False   |render gif   |
+
+
+We have also prepared scripts with different configurations for reference
+- Inference Std-pipeline requires 30GB VRAM (24G VRAM with --save_memory).
+- Inference Lite-pipeline requires 22GB VRAM (18G VRAM with --save_memory).
+- Note: --save_memory will increase inference time
+
+```bash
+bash scripts/text_to_3d_std.sh 
+bash scripts/text_to_3d_lite.sh 
+bash scripts/image_to_3d_std.sh 
+bash scripts/image_to_3d_lite.sh 
+```
+
+If your gpu memory is 16G, you can try to run modules in pipeline seperately:
+```bash
+bash scripts/text_to_3d_std_separately.sh 'a lovely rabbit' ./outputs/test # >= 16G
+bash scripts/text_to_3d_lite_separately.sh 'a lovely rabbit' ./outputs/test # >= 14G
+bash scripts/image_to_3d_std_separately.sh ./demos/example_000.png ./outputs/test  # >= 16G
+bash scripts/image_to_3d_lite_separately.sh ./demos/example_000.png ./outputs/test # >= 10G
+```
+
+#### Using Gradio
+
+We have prepared two versions of multi-view generation, std and lite.
+
+```shell
+# std 
+python3 app.py
+python3 app.py --save_memory
+
+# lite
+python3 app.py --use_lite
+python3 app.py --use_lite --save_memory
+```
+
+Then the demo can be accessed through http://0.0.0.0:8080. It should be noted that the 0.0.0.0 here needs to be X.X.X.X with your server IP.
+
+## Camera Parameters
+
+Output views are a fixed set of camera poses:
+
++ Azimuth (relative to input view): `+0, +60, +120, +180, +240, +300`.
+
+
+## Citation
+
+If you found this repository helpful, please cite our report:
+```bibtex
+@misc{yang2024tencent,
+    title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
+    author={Xianghui Yang and Huiwen Shi and Bowen Zhang and Fan Yang and Jiacheng Wang and Hongxu Zhao and Xinhai Liu and Xinzhou Wang and Qingxiang Lin and Jiaao Yu and Lifu Wang and Zhuo Chen and Sicong Liu and Yuhong Liu and Yong Yang and Di Wang and Jie Jiang and Chunchao Guo},
+    year={2024},
+    eprint={2411.02293},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```","{""id"": ""tencent/Hunyuan3D-1"", ""author"": ""tencent"", ""sha"": ""e0315a42d8a0f5a63e57abeace1737d34d700109"", ""last_modified"": ""2025-03-22 13:37:23+00:00"", ""created_at"": ""2024-11-01 08:42:28+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 3230, ""downloads_all_time"": null, ""likes"": 301, ""library_name"": ""hunyuan3d-2"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""hunyuan3d-2"", ""diffusers"", ""safetensors"", ""image-to-3d"", ""text-to-3d"", ""en"", ""zh"", ""arxiv:2411.02293"", ""license:other"", ""region:us""], ""pipeline_tag"": ""image-to-3d"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\n- zh\nlibrary_name: hunyuan3d-2\nlicense: other\nlicense_name: tencent-hunyuan-community\nlicense_link: https://huggingface.co/tencent/Hunyuan3D-1/blob/main/LICENSE.txt\npipeline_tag: image-to-3d\ntags:\n- image-to-3d\n- text-to-3d"", ""widget_data"": null, 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+img-gemina/text2speech,"---
+license: mit
+datasets:
+- BAAI/Infinity-MM
+language:
+- id
+metrics:
+- accuracy
+base_model:
+- tencent/Hunyuan3D-1
+new_version: microsoft/OmniParser
+library_name: transformers
+---","{""id"": ""img-gemina/text2speech"", ""author"": ""img-gemina"", ""sha"": ""82332d1849ff2f5137a50be6a468586aa092884c"", ""last_modified"": ""2024-11-22 11:28:44+00:00"", ""created_at"": ""2024-11-22 11:25:52+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""id"", ""dataset:BAAI/Infinity-MM"", ""base_model:tencent/Hunyuan3D-1"", ""base_model:finetune:tencent/Hunyuan3D-1"", ""license:mit"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- tencent/Hunyuan3D-1\ndatasets:\n- BAAI/Infinity-MM\nlanguage:\n- id\nlibrary_name: transformers\nlicense: mit\nmetrics:\n- accuracy\nnew_version: microsoft/OmniParser"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='train.py', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-11-22 11:28:44+00:00"", ""cardData"": ""base_model:\n- tencent/Hunyuan3D-1\ndatasets:\n- BAAI/Infinity-MM\nlanguage:\n- id\nlibrary_name: transformers\nlicense: mit\nmetrics:\n- accuracy\nnew_version: microsoft/OmniParser"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""67406a40125bcd870384b481"", ""modelId"": ""img-gemina/text2speech"", ""usedStorage"": 0}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=img-gemina/text2speech&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bimg-gemina%2Ftext2speech%5D(%2Fimg-gemina%2Ftext2speech)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Hunyuan3D-2mv_finetunes_20250426_014322.csv_finetunes_20250426_014322.csv

@@ -0,0 +1,113 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+tencent/Hunyuan3D-2mv,"---
+library_name: hunyuan3d-2
+license: other
+license_name: tencent-hunyuan-community
+license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt
+language:
+  - en
+  - zh
+tags:
+  - image-to-3d
+  - text-to-3d
+pipeline_tag: image-to-3d
+---
+
+<p align=""center"">
+  <img src=""https://huggingface.co/tencent/Hunyuan3D-2/resolve/main/assets/images/teaser.jpg"">
+</p>
+
+<div align=""center"">
+  <a href=https://3d.hunyuan.tencent.com target=""_blank""><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a>
+  <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2mv  target=""_blank""><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
+  <a href=https://huggingface.co/tencent/Hunyuan3D-2mv target=""_blank""><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
+  <a href=https://github.com/Tencent/Hunyuan3D-2 target=""_blank""><img src= https://img.shields.io/badge/Github-bb8a2e.svg?logo=github height=22px></a>
+<a href=https://discord.gg/GuaWYwzKbX target=""_blank""><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a>
+    <a href=https://github.com/Tencent/Hunyuan3D-2/blob/main/assets/report/Tencent_Hunyuan3D_2_0.pdf target=""_blank""><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
+</div>
+
+
+[//]: # (  <a href=# target=""_blank""><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>)
+
+[//]: # (  <a href=# target=""_blank""><img src= https://img.shields.io/badge/Colab-8f2628.svg?logo=googlecolab height=22px></a>)
+
+[//]: # (  <a href=""#""><img alt=""PyPI - Downloads"" src=""https://img.shields.io/pypi/v/mulankit?logo=pypi""  height=22px></a>)
+
+<br>
+<p align=""center"">
+“ Living out everyone’s imagination on creating and manipulating 3D assets.”
+</p>
+
+This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202).
+
+**Hunyuan3D-2mv** is finetuned from [Hunyuan3D-2](https://huggingface.co/tencent/Hunyuan3D-2) to support multiview controlled shape generation.
+
+## 🤗 Get Started with Hunyuan3D 2mv
+
+Here is a simple usage:
+
+```python
+from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
+pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
+    'tencent/Hunyuan3D-2mv',
+    subfolder='hunyuan3d-dit-v2-mv',
+    use_safetensors=True,
+    device='cuda'
+)
+mesh = pipeline(
+    image={
+        ""front"": ""your front view image.png"",
+        ""left"": ""your left view image.png"",
+        ""back"": ""your back view image.png""
+    },
+    num_inference_steps=30,
+    octree_resolution=380,
+    num_chunks=20000,
+    generator=torch.manual_seed(12345),
+    output_type='trimesh'
+)[0]
+```
+
+For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2).
+
+
+
+## 🔗 BibTeX
+
+If you found this repository helpful, please cite our report:
+
+```bibtex
+@misc{hunyuan3d22025tencent,
+    title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
+    author={Tencent Hunyuan3D Team},
+    year={2025},
+    eprint={2501.12202},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{yang2024tencent,
+    title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
+    author={Tencent Hunyuan3D Team},
+    year={2024},
+    eprint={2411.02293},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+## Community Resources
+
+Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0:
+
+- [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper)
+- [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows)
+- [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2)
+
+## Acknowledgements
+
+We would like to thank the contributors to
+the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers)
+and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
+
+","{""id"": ""tencent/Hunyuan3D-2mv"", ""author"": ""tencent"", ""sha"": ""ea1415a196ba61f465e923072172713aa023e6b0"", ""last_modified"": ""2025-03-19 16:12:59+00:00"", ""created_at"": ""2025-03-12 11:36:17+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 9470, ""downloads_all_time"": null, ""likes"": 371, ""library_name"": ""hunyuan3d-2"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""hunyuan3d-2"", ""image-to-3d"", ""text-to-3d"", ""en"", ""zh"", ""arxiv:2501.12202"", ""arxiv:2411.02293"", ""license:other"", ""region:us""], ""pipeline_tag"": ""image-to-3d"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\n- zh\nlibrary_name: hunyuan3d-2\nlicense: other\nlicense_name: tencent-hunyuan-community\nlicense_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt\npipeline_tag: image-to-3d\ntags:\n- image-to-3d\n- text-to-3d"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='NOTICE', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-fast/config.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-fast/model.fp16.ckpt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-fast/model.fp16.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-turbo/config.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-turbo/model.fp16.ckpt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv-turbo/model.fp16.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv/config.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv/model.fp16.ckpt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan3d-dit-v2-mv/model.fp16.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [""tencent/Hunyuan3D-2mv""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-03-19 16:12:59+00:00"", ""cardData"": ""language:\n- en\n- zh\nlibrary_name: hunyuan3d-2\nlicense: other\nlicense_name: tencent-hunyuan-community\nlicense_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt\npipeline_tag: image-to-3d\ntags:\n- image-to-3d\n- text-to-3d"", ""transformersInfo"": null, ""_id"": ""67d171b18da5ca753ec1334e"", ""modelId"": ""tencent/Hunyuan3D-2mv"", ""usedStorage"": 29580814970}",0,,0,,0,,0,,0,"huggingface/InferenceSupport/discussions/143, tencent/Hunyuan3D-2mv",2

HunyuanVideo-I2V_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,524 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+tencent/HunyuanVideo-I2V,"---
+license: other
+license_name: tencent-hunyuan-community
+license_link: LICENSE
+pipeline_tag: image-to-video
+---
+<!-- ## **HunyuanVideo** -->
+
+[中文阅读](./README_zh.md)
+
+<p align=""center"">
+  <img src=""https://raw.githubusercontent.com/Tencent/HunyuanVideo-I2V/refs/heads/main/assets/logo.png""  height=100>
+</p>
+
+# **HunyuanVideo-I2V** 🌅
+
+-----
+
+Following the great successful open-sourcing of our [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), we proudly present the [HunyuanVideo-I2V](https://github.com/Tencent/HunyuanVideo-I2V), a new image-to-video generation framework to accelerate open-source community exploration!
+
+This repo contains offical PyTorch model definitions, pre-trained weights and inference/sampling code. You can find more visualizations on our [project page](https://aivideo.hunyuan.tencent.com). Meanwhile, we have released the LoRA training code for customizable special effects, which can be used to create more interesting video effects.
+
+> [**HunyuanVideo: A Systematic Framework For Large Video Generation Model**](https://arxiv.org/abs/2412.03603) <be>
+
+
+
+## 🔥🔥🔥 News!!
+* Mar 13, 2025: 🚀 We release the parallel inference code for HunyuanVideo-I2V powered by [xDiT](https://github.com/xdit-project/xDiT).
+* Mar 07, 2025: 🔥 We have fixed the bug in our open-source version that caused ID changes. Please try the new model weights of [HunyuanVideo-I2V](https://huggingface.co/tencent/HunyuanVideo-I2V) to ensure full visual consistency in the first frame and produce higher quality videos. 
+* Mar 06, 2025: 👋 We release the inference code and model weights of HunyuanVideo-I2V. [Download](https://github.com/Tencent/HunyuanVideo-I2V/blob/main/ckpts/README.md).
+
+
+## 📑 Open-source Plan
+- HunyuanVideo-I2V (Image-to-Video Model)
+  - [x] Inference 
+  - [x] Checkpoints
+  - [x] ComfyUI
+  - [x] Lora training scripts
+  - [x] Multi-gpus Sequence Parallel inference (Faster inference speed on more gpus)
+  - [ ] Diffusers
+
+## Contents
+- [**HunyuanVideo-I2V** 🌅](#hunyuanvideo-i2v-)
+  - [🔥🔥🔥 News!!](#-news)
+  - [📑 Open-source Plan](#-open-source-plan)
+  - [Contents](#contents)
+  - [**HunyuanVideo-I2V Overall Architecture**](#hunyuanvideo-i2v-overall-architecture)
+  - [📜 Requirements](#-requirements)
+  - [🛠️ Dependencies and Installation](#️-dependencies-and-installation)
+    - [Installation Guide for Linux](#installation-guide-for-linux)
+  - [🧱 Download Pretrained Models](#-download-pretrained-models)
+  - [🔑 Single-gpu Inference](#-single-gpu-inference)
+    - [Tips for Using Image-to-Video Models](#tips-for-using-image-to-video-models)
+    - [Using Command Line](#using-command-line)
+    - [More Configurations](#more-configurations)
+  - [🎉 Customizable I2V LoRA effects training](#-customizable-i2v-lora-effects-training)
+    - [Requirements](#requirements)
+    - [Environment](#environment)
+    - [Training data construction](#training-data-construction)
+    - [Training](#training)
+    - [Inference](#inference)
+  - [🚀 Parallel Inference on Multiple GPUs by xDiT](#-parallel-inference-on-multiple-gpus-by-xdit)
+    - [Using Command Line](#using-command-line-1)
+  - [🔗 BibTeX](#-bibtex)
+  - [Acknowledgements](#acknowledgements)
+---
+
+## **HunyuanVideo-I2V Overall Architecture**
+Leveraging the advanced video generation capabilities of [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), we have extended its application to image-to-video generation tasks. To achieve this, we employ a token replace technique to effectively reconstruct and incorporate reference image information into the video generation process.
+
+Since we utilizes a pre-trained Multimodal Large Language Model (MLLM) with a Decoder-Only architecture as the text encoder, we can significantly enhance the model's ability to comprehend the semantic content of the input image and to seamlessly integrate information from both the image and its associated caption. Specifically, the input image is processed by the MLLM to generate semantic image tokens. These tokens are then concatenated with the video latent tokens, enabling comprehensive full-attention computation across the combined data.
+
+The overall architecture of our system is designed to maximize the synergy between image and text modalities, ensuring a robust and coherent generation of video content from static images. This integration not only improves the fidelity of the generated videos but also enhances the model's ability to interpret and utilize complex multimodal inputs. The overall architecture is as follows.
+<p align=""center"">
+  <img src=""https://raw.githubusercontent.com/Tencent/HunyuanVideo-I2V/refs/heads/main/assets/backbone.png""  style=""max-width: 45%; height: auto;"">
+</p>
+
+
+
+
+## 📜 Requirements
+
+The following table shows the requirements for running HunyuanVideo-I2V model (batch size = 1) to generate videos:
+
+|      Model       | Resolution  | GPU Peak Memory  |
+|:----------------:|:-----------:|:----------------:|
+| HunyuanVideo-I2V |    720p     |       60GB        |
+
+
+* An NVIDIA GPU with CUDA support is required. 
+  * The model is tested on a single 80G GPU.
+  * **Minimum**: The minimum GPU memory required is 60GB for 720p.
+  * **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
+* Tested operating system: Linux
+
+## 🛠️ Dependencies and Installation
+
+Begin by cloning the repository:
+```shell
+git clone https://github.com/tencent/HunyuanVideo-I2V
+cd HunyuanVideo-I2V
+```
+
+### Installation Guide for Linux
+
+We recommend CUDA versions 12.4 or 11.8 for the manual installation.
+
+Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
+
+```shell
+# 1. Create conda environment
+conda create -n HunyuanVideo-I2V python==3.11.9
+
+# 2. Activate the environment
+conda activate HunyuanVideo-I2V
+
+# 3. Install PyTorch and other dependencies using conda
+# For CUDA 12.4
+conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
+
+# 4. Install pip dependencies
+python -m pip install -r requirements.txt
+
+# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
+python -m pip install ninja
+python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
+
+# 6. Install xDiT for parallel inference (It is recommended to use torch 2.4.0 and flash-attn 2.6.3)
+python -m pip install xfuser==0.4.0
+```
+
+In case of running into float point exception(core dump) on the specific GPU type, you may try the following solutions:
+
+```shell
+# Making sure you have installed CUDA 12.4, CUBLAS>=12.4.5.8, and CUDNN>=9.00 (or simply using our CUDA 12 docker image).
+pip install nvidia-cublas-cu12==12.4.5.8
+export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
+```
+
+Additionally, HunyuanVideo-I2V also provides a pre-built Docker image. Use the following command to pull and run the docker image.
+
+```shell
+# For CUDA 12.4 (updated to avoid float point exception)
+docker pull hunyuanvideo/hunyuanvideo-i2v:cuda12
+docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo-i2v --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo-i2v:cuda12
+```
+
+
+## 🧱 Download Pretrained Models
+
+The details of download pretrained models are shown [here](ckpts/README.md).
+
+
+
+## 🔑 Single-gpu Inference
+
+Similar to [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), HunyuanVideo-I2V supports high-resolution video generation, with resolution up to 720P and video length up to 129 frames (5 seconds).
+
+### Tips for Using Image-to-Video Models
+- **Use Concise Prompts**: To effectively guide the model's generation, keep your prompts short and to the point.
+- **Include Key Elements**: A well-structured prompt should cover:
+  - **Main Subject**: Specify the primary focus of the video.
+  - **Action**: Describe the main movement or activity taking place.
+  - **Background (Optional)**: Set the scene for the video.
+  - **Camera Angle (Optional)**: Indicate the perspective or viewpoint.
+- **Avoid Overly Detailed Prompts**: Lengthy or highly detailed prompts can lead to unnecessary transitions in the video output.
+
+<!-- **For image-to-video models, we recommend using concise prompts to guide the model's generation process. A good prompt should include elements such as background, main subject, action, and camera angle. Overly long or excessively detailed prompts may introduce unnecessary transitions.** -->
+
+### Using Command Line
+
+<!-- ### Run a Gradio Server
+```bash
+python3 gradio_server.py --flow-reverse
+
+# set SERVER_NAME and SERVER_PORT manually
+# SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
+``` -->
+If you want to generate a more **stable** video, you can set `--i2v-stability` and `--flow-shift 7.0`. Execute the command as follows
+```bash
+cd HunyuanVideo-I2V
+
+python3 sample_image2video.py \
+    --model HYVideo-T/2 \
+    --prompt ""An Asian man with short hair in black tactical uniform and white clothes waves a firework stick."" \
+    --i2v-mode \
+    --i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
+    --i2v-resolution 720p \
+    --i2v-stability \
+    --infer-steps 50 \
+    --video-length 129 \
+    --flow-reverse \
+    --flow-shift 7.0 \
+    --seed 0 \
+    --embedded-cfg-scale 6.0 \
+    --use-cpu-offload \
+    --save-path ./results
+```
+If you want to generate a more **high-dynamic** video, you can **unset** `--i2v-stability` and `--flow-shift 17.0`. Execute the command as follows
+```bash
+cd HunyuanVideo-I2V
+
+python3 sample_image2video.py \
+    --model HYVideo-T/2 \
+    --prompt ""An Asian man with short hair in black tactical uniform and white clothes waves a firework stick."" \
+    --i2v-mode \
+    --i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
+    --i2v-resolution 720p \
+    --infer-steps 50 \
+    --video-length 129 \
+    --flow-reverse \
+    --flow-shift 17.0 \
+    --embedded-cfg-scale 6.0 \
+    --seed 0 \
+    --use-cpu-offload \
+    --save-path ./results
+```
+### More Configurations
+
+We list some more useful configurations for easy usage:
+
+|        Argument        |           Default            |                                                                                        Description                                                                                        |
+|:----------------------:|:----------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+|       `--prompt`       |             None             |                                                                           The text prompt for video generation.                                                                           |
+|       `--model`        |    HYVideo-T/2-cfgdistill    |                                                       Here we use HYVideo-T/2 for I2V, HYVideo-T/2-cfgdistill is used for T2V mode.                                                       |
+|     `--i2v-mode`       |            False             |                                                                                 Whether to open i2v mode.                                                                                 |
+|  `--i2v-image-path`    | ./assets/demo/i2v/imgs/0.jpg |                                                                         The reference image for video generation.                                                                         |
+|  `--i2v-resolution`    |             720p             |                                                                          The resolution for the generated video.                                                                          |
+|  `--i2v-stability`    |            False             |                                                                       Whether to use stable mode for i2v inference.                                                                       |
+|    `--video-length`    |             129              |                                                                            The length of the generated video.                                                                             |
+|    `--infer-steps`     |              50              |                                                                             The number of steps for sampling.                                                                             |
+|     `--flow-shift`     |             7.0              | Shift factor for flow matching schedulers. We recommend 7 with `--i2v-stability` switch on for more stable video, 17 with `--i2v-stability` switch off for more dynamic video |
+|   `--flow-reverse`     |            False             |                                                                      If reverse, learning/sampling from t=1 -> t=0.                                                                       |
+|        `--seed`        |             None             |                                             The random seed for generating video, if None, we init a random seed.                                                                         |
+|  `--use-cpu-offload`   |            False             |                                             Use CPU offload for the model load to save more memory, necessary for high-res video generation.                                              |
+|     `--save-path`      |          ./results           |                                                                             Path to save the generated video.                                                                             |
+
+
+
+## 🎉 Customizable I2V LoRA effects training
+
+### Requirements
+
+The following table shows the requirements for training HunyuanVideo-I2V lora model (batch size = 1) to generate videos:
+
+|      Model       | Resolution | GPU Peak Memory |
+|:----------------:|:----------:|:---------------:|
+| HunyuanVideo-I2V |    360p    |      79GB       |
+
+* An NVIDIA GPU with CUDA support is required. 
+  * The model is tested on a single 80G GPU.
+  * **Minimum**: The minimum GPU memory required is 79GB for 360p.
+  * **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
+* Tested operating system: Linux
+* Note: You can train with 360p data and directly infer 720p videos
+
+### Environment
+```
+pip install -r requirements.txt
+```
+
+### Training data construction
+Prompt description: The trigger word is written directly in the video caption. It is recommended to use a phrase or short sentence.
+
+For example, AI hair growth effect (trigger): rapid_hair_growth, The hair of the characters in the video is growing rapidly. + original prompt
+
+After having the training video and prompt pair, refer to [here] (hyvideo/hyvae_extract/README.md) for training data construction.
+
+
+### Training
+```
+cd HunyuanVideo-I2V
+
+sh scripts/run_train_image2video_lora.sh
+```
+We list some training specific configurations for easy usage:
+
+|     Argument     |                            Default                            |                         Description                         |
+|:----------------:|:-------------------------------------------------------------:|:-----------------------------------------------------------:|
+|   `SAVE_BASE`    |                               .                               |         Root path for saving experimental results.          |
+|    `EXP_NAME`    |                           i2v_lora                            |        Path suffix for saving experimental results.         |
+| `DATA_JSONS_DIR` | ./assets/demo/i2v_lora/train_dataset/processed_data/json_path | Data jsons dir generated by hyvideo/hyvae_extract/start.sh. |
+|    `CHIEF_IP`    |                           127.0.0.1                           |            Master node IP of the machine.                   |
+
+After training, you can find `pytorch_lora_kohaya_weights.safetensors` in `{SAVE_BASE}/log_EXP/*_{EXP_NAME}/checkpoints/global_step{*}/pytorch_lora_kohaya_weights.safetensors` and set it in `--lora-path` to perform inference.
+
+### Inference
+```bash
+cd HunyuanVideo-I2V
+
+python3 sample_image2video.py \
+   --model HYVideo-T/2 \
+   --prompt ""Two people hugged tightly, In the video, two people are standing apart from each other. They then move closer to each other and begin to hug tightly. The hug is very affectionate, with the two people holding each other tightly and looking into each other's eyes. The interaction is very emotional and heartwarming, with the two people expressing their love and affection for each other."" \
+   --i2v-mode \
+   --i2v-image-path ./assets/demo/i2v_lora/imgs/embrace.png \
+   --i2v-resolution 720p \
+   --i2v-stability \
+   --infer-steps 50 \
+   --video-length 129 \
+   --flow-reverse \
+   --flow-shift 5.0 \
+   --embedded-cfg-scale 6.0 \
+   --seed 0 \
+   --use-cpu-offload \
+   --save-path ./results \
+   --use-lora \
+   --lora-scale 1.0 \
+   --lora-path ./ckpts/hunyuan-video-i2v-720p/lora/embrace_kohaya_weights.safetensors
+```
+We list some lora specific configurations for easy usage:
+
+|      Argument       | Default |         Description          |
+|:-------------------:|:-------:|:----------------------------:|
+|    `--use-lora`     |  False  |  Whether to open lora mode.  |
+|   `--lora-scale`    |   1.0   | Fusion scale for lora model. |
+|   `--lora-path`     |   """"    |  Weight path for lora model. |
+
+## 🚀 Parallel Inference on Multiple GPUs by xDiT
+
+[xDiT](https://github.com/xdit-project/xDiT) is a Scalable Inference Engine for Diffusion Transformers (DiTs) on multi-GPU Clusters.
+It has successfully provided low-latency parallel inference solutions for a variety of DiTs models, including mochi-1, CogVideoX, Flux.1, SD3, etc. This repo adopted the [Unified Sequence Parallelism (USP)](https://arxiv.org/abs/2405.07719) APIs for parallel inference of the HunyuanVideo-I2V model.
+
+### Using Command Line
+
+For example, to generate a video with 8 GPUs, you can use the following command:
+
+```bash
+cd HunyuanVideo-I2V
+
+torchrun --nproc_per_node=8 sample_image2video.py \
+    --model HYVideo-T/2 \
+    --prompt ""An Asian man with short hair in black tactical uniform and white clothes waves a firework stick."" \
+    --i2v-mode \
+    --i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
+    --i2v-resolution 720p \
+    --i2v-stability \
+    --infer-steps 50 \
+    --video-length 129 \
+    --flow-reverse \
+    --flow-shift 7.0 \
+    --seed 0 \
+    --embedded-cfg-scale 6.0 \
+    --save-path ./results \
+    --ulysses-degree 8 \
+    --ring-degree 1 \
+    --video-size 1280 720 \
+    --xdit-adaptive-size
+```
+
+You can change the `--ulysses-degree` and `--ring-degree` to control the parallel configurations for the best performance. 
+Note that you need to set `--video-size` since xDiT's acceleration mechanism has requirements for the size of the video to be generated.
+To prevent black padding after converting the original image height/width to the target height/width, you can use `--xdit-adaptive-size`.
+The valid parallel configurations are shown in the following table.
+
+<details>
+<summary>Supported Parallel Configurations (Click to expand)</summary>
+
+|     --video-size     | --video-length | --ulysses-degree x --ring-degree | --nproc_per_node |
+|----------------------|----------------|----------------------------------|------------------|
+| 1280 720 or 720 1280 | 129            | 8x1,4x2,2x4,1x8                  | 8                |
+| 1280 720 or 720 1280 | 129            | 1x5                              | 5                |
+| 1280 720 or 720 1280 | 129            | 4x1,2x2,1x4                      | 4                |
+| 1280 720 or 720 1280 | 129            | 3x1,1x3                          | 3                |
+| 1280 720 or 720 1280 | 129            | 2x1,1x2                          | 2                |
+| 1104 832 or 832 1104 | 129            | 4x1,2x2,1x4                      | 4                |
+| 1104 832 or 832 1104 | 129            | 3x1,1x3                          | 3                |
+| 1104 832 or 832 1104 | 129            | 2x1,1x2                          | 2                |
+| 960 960              | 129            | 6x1,3x2,2x3,1x6                  | 6                |
+| 960 960              | 129            | 4x1,2x2,1x4                      | 4                |
+| 960 960              | 129            | 3x1,1x3                          | 3                |
+| 960 960              | 129            | 1x2,2x1                          | 2                |
+| 960 544 or 544 960   | 129            | 6x1,3x2,2x3,1x6                  | 6                |
+| 960 544 or 544 960   | 129            | 4x1,2x2,1x4                      | 4                |
+| 960 544 or 544 960   | 129            | 3x1,1x3                          | 3                |
+| 960 544 or 544 960   | 129            | 1x2,2x1                          | 2                |
+| 832 624 or 624 832   | 129            | 4x1,2x2,1x4                      | 4                |
+| 624 832 or 624 832   | 129            | 3x1,1x3                          | 3                |
+| 832 624 or 624 832   | 129            | 2x1,1x2                          | 2                |
+| 720 720              | 129            | 1x5                              | 5                |
+| 720 720              | 129            | 3x1,1x3                          | 3                |
+
+</details>
+
+
+<p align=""center"">
+<table align=""center"">
+<thead>
+<tr>
+    <th colspan=""4"">Latency (Sec) for 1280x720 (129 frames 50 steps) on 8xGPU</th>
+</tr>
+<tr>
+    <th>1</th>
+    <th>2</th>
+    <th>4</th>
+    <th>8</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+    <th>1904.08</th>
+    <th>934.09 (2.04x)</th>
+    <th>514.08 (3.70x)</th>
+    <th>337.58 (5.64x)</th>
+</tr>
+
+</tbody>
+</table>
+</p>
+
+
+## 🔗 BibTeX
+
+If you find [HunyuanVideo](https://arxiv.org/abs/2412.03603) useful for your research and applications, please cite using this BibTeX:
+
+```BibTeX
+@misc{kong2024hunyuanvideo,
+      title={HunyuanVideo: A Systematic Framework For Large Video Generative Models}, 
+      author={Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Junkun Yuan, Kai Wang, Mengyang Liu, Pengyu Li, Shuai Li, Weiyan Wang, Wenqing Yu, Xinchi Deng, Yang Li, Yanxin Long, Yi Chen, Yutao Cui, Yuanbo Peng, Zhentao Yu, Zhiyu He, Zhiyong Xu, Zixiang Zhou, Zunnan Xu, Yangyu Tao, Qinglin Lu, Songtao Liu, Dax Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, and Jie Jiang, along with Caesar Zhong},
+      year={2024},
+      archivePrefix={arXiv preprint arXiv:2412.03603},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2412.03603}, 
+}
+```
+
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
+Additionally, we also thank the Tencent Hunyuan Multimodal team for their help with the text encoder. 
+
+
+
+
+<!-- ## Github Star History
+<a href=""https://star-history.com/#Tencent/HunyuanVideo&Date"">
+ <picture>
+   <source media=""(prefers-color-scheme: dark)"" srcset=""https://api.star-history.com/svg?repos=Tencent/HunyuanVideo&type=Date&theme=dark"" />
+   <source media=""(prefers-color-scheme: light)"" srcset=""https://api.star-history.com/svg?repos=Tencent/HunyuanVideo&type=Date"" />
+   <img alt=""Star History Chart"" src=""https://api.star-history.com/svg?repos=Tencent/HunyuanVideo&type=Date"" />
+ </picture>
+</a> -->
+","{""id"": ""tencent/HunyuanVideo-I2V"", ""author"": ""tencent"", ""sha"": ""3914f209367854b5e470f062c33159d5ab139e1e"", ""last_modified"": ""2025-03-13 10:36:22+00:00"", ""created_at"": ""2025-03-05 09:27:03+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 3309, ""downloads_all_time"": null, ""likes"": 305, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""image-to-video"", ""arxiv:2412.03603"", ""arxiv:2405.07719"", ""license:other"", ""region:us""], ""pipeline_tag"": ""image-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: other\nlicense_name: tencent-hunyuan-community\nlicense_link: LICENSE\npipeline_tag: image-to-video"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='Notice', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan-video-i2v-720p/lora/embrace_kohaya_weights.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan-video-i2v-720p/lora/hair_growth_kohaya_weights.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan-video-i2v-720p/vae/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='hunyuan-video-i2v-720p/vae/pytorch_model.pt', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-03-13 10:36:22+00:00"", ""cardData"": ""license: other\nlicense_name: tencent-hunyuan-community\nlicense_link: LICENSE\npipeline_tag: image-to-video"", ""transformersInfo"": null, ""_id"": ""67c818e729514343cee6eb43"", ""modelId"": ""tencent/HunyuanVideo-I2V"", ""usedStorage"": 59395190150}",0,"https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V, https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V-33ch",2,,0,"https://huggingface.co/city96/HunyuanVideo-I2V-gguf, https://huggingface.co/calcuis/hyvid-i2v-gguf, https://huggingface.co/hanzogak/HunyuanVideo-I2V-gguf",3,,0,huggingface/InferenceSupport/discussions/1103,1
+hunyuanvideo-community/HunyuanVideo-I2V,"---
+base_model:
+- tencent/HunyuanVideo-I2V
+library_name: diffusers
+pipeline_tag: image-to-video
+---
+
+Unofficial community fork for Diffusers-format weights on [`tencent/HunyuanVideo-I2V`](https://huggingface.co/tencent/HunyuanVideo-I2V).
+
+### Using Diffusers
+
+HunyuanVideo-I2V can be used directly from Diffusers. Install the latest version of Diffusers.
+
+```python
+import torch
+from diffusers import HunyuanVideoImageToVideoPipeline, HunyuanVideoTransformer3DModel
+from diffusers.utils import load_image, export_to_video
+
+# Available checkpoints: ""hunyuanvideo-community/HunyuanVideo-I2V"" and ""hunyuanvideo-community/HunyuanVideo-I2V-33ch""
+model_id = ""hunyuanvideo-community/HunyuanVideo-I2V""
+transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+    model_id, subfolder=""transformer"", torch_dtype=torch.bfloat16
+)
+pipe = HunyuanVideoImageToVideoPipeline.from_pretrained(
+    model_id, transformer=transformer, torch_dtype=torch.float16
+)
+pipe.vae.enable_tiling()
+pipe.to(""cuda"")
+
+prompt = ""A man with short gray hair plays a red electric guitar.""
+image = load_image(
+    ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png""
+)
+
+output = pipe(image=image, prompt=prompt).frames[0]
+export_to_video(output, ""output.mp4"", fps=15)
+```
+
+Refer to the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video) for more information.","{""id"": ""hunyuanvideo-community/HunyuanVideo-I2V"", ""author"": ""hunyuanvideo-community"", ""sha"": ""fb9d287ef02fe6d39f2e23df6dcec1294e6c28d2"", ""last_modified"": ""2025-03-19 16:17:53+00:00"", ""created_at"": ""2025-03-19 16:11:23+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 563, ""downloads_all_time"": null, ""likes"": 2, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""image-to-video"", ""base_model:tencent/HunyuanVideo-I2V"", ""base_model:finetune:tencent/HunyuanVideo-I2V"", ""diffusers:HunyuanVideoImageToVideoPipeline"", ""region:us""], ""pipeline_tag"": ""image-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- tencent/HunyuanVideo-I2V\nlibrary_name: 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lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/vocab.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00001-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00002-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00003-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00004-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00005-of-00005.safetensors', 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+hunyuanvideo-community/HunyuanVideo-I2V-33ch,"---
+base_model:
+- tencent/HunyuanVideo-I2V
+library_name: diffusers
+---
+
+Unofficial community fork for Diffusers-format weights on [`tencent/HunyuanVideo-I2V`](https://huggingface.co/tencent/HunyuanVideo-I2V).
+
+### Using Diffusers
+
+HunyuanVideo-I2V can be used directly from Diffusers. Install the latest version of Diffusers.
+
+```python
+import torch
+from diffusers import HunyuanVideoImageToVideoPipeline, HunyuanVideoTransformer3DModel
+from diffusers.utils import load_image, export_to_video
+
+# Available checkpoints: ""hunyuanvideo-community/HunyuanVideo-I2V"" and ""hunyuanvideo-community/HunyuanVideo-I2V-33ch""
+model_id = ""hunyuanvideo-community/HunyuanVideo-I2V-33ch""
+transformer = HunyuanVideoTransformer3DModel.from_pretrained(
+    model_id, subfolder=""transformer"", torch_dtype=torch.bfloat16
+)
+pipe = HunyuanVideoImageToVideoPipeline.from_pretrained(
+    model_id, transformer=transformer, torch_dtype=torch.float16
+)
+pipe.vae.enable_tiling()
+pipe.to(""cuda"")
+
+prompt = ""A man with short gray hair plays a red electric guitar.""
+image = load_image(
+    ""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png""
+)
+
+output = pipe(image=image, prompt=prompt).frames[0]
+export_to_video(output, ""output.mp4"", fps=15)
+```
+
+Refer to the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video) for more information.
+
+","{""id"": ""hunyuanvideo-community/HunyuanVideo-I2V-33ch"", ""author"": ""hunyuanvideo-community"", ""sha"": ""fddc4df6b7fcc55080ba903e88ca62804f4dd479"", ""last_modified"": ""2025-03-20 05:26:24+00:00"", ""created_at"": ""2025-03-06 21:48:37+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 30, ""downloads_all_time"": null, ""likes"": 6, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""base_model:tencent/HunyuanVideo-I2V"", ""base_model:finetune:tencent/HunyuanVideo-I2V"", ""diffusers:HunyuanVideoImageToVideoPipeline"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- tencent/HunyuanVideo-I2V\nlibrary_name: diffusers"", ""widget_data"": null, ""model_index"": null, ""config"": {""diffusers"": {""_class_name"": ""HunyuanVideoImageToVideoPipeline""}}, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='image_processor/preprocessor_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model_index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='scheduler/scheduler_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model-00001-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model-00002-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model-00003-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model-00004-of-00004.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder_2/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder_2/model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_2/vocab.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00001-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00002-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00003-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00004-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model-00005-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/diffusion_pytorch_model.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-03-20 05:26:24+00:00"", ""cardData"": ""base_model:\n- tencent/HunyuanVideo-I2V\nlibrary_name: diffusers"", ""transformersInfo"": null, ""_id"": ""67ca1835b73e313ed13ffce9"", ""modelId"": ""hunyuanvideo-community/HunyuanVideo-I2V-33ch"", ""usedStorage"": 43624028374}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=hunyuanvideo-community/HunyuanVideo-I2V-33ch&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bhunyuanvideo-community%2FHunyuanVideo-I2V-33ch%5D(%2Fhunyuanvideo-community%2FHunyuanVideo-I2V-33ch)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

IP-Adapter_finetunes_20250424_223250.csv_finetunes_20250424_223250.csv

@@ -0,0 +1,48 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+h94/IP-Adapter,"---
+tags:
+- text-to-image
+- stable-diffusion
+license: apache-2.0
+language:
+- en
+library_name: diffusers
+---
+
+# IP-Adapter Model Card
+
+
+<div align=""center"">
+
+[**Project Page**](https://ip-adapter.github.io) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2308.06721) **|** [**Code**](https://github.com/tencent-ailab/IP-Adapter)
+</div>
+
+---
+
+
+## Introduction
+
+we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pre-trained text-to-image diffusion models. An IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fine-tuned image prompt model. IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. Moreover, the image prompt can also work well with the text prompt to accomplish multimodal image generation.
+
+![arch](./fig1.png)
+
+## Models
+
+### Image Encoder
+- [models/image_encoder](https://huggingface.co/h94/IP-Adapter/tree/main/models/image_encoder): [OpenCLIP-ViT-H-14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) with 632.08M parameter
+- [sdxl_models/image_encoder](https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models/image_encoder): [OpenCLIP-ViT-bigG-14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) with 1844.9M parameter
+
+More information can be found [here](https://laion.ai/blog/giant-openclip/)
+
+### IP-Adapter for SD 1.5
+- [ip-adapter_sd15.bin](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter_sd15.bin): use global image embedding from OpenCLIP-ViT-H-14 as condition
+- [ip-adapter_sd15_light.bin](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter_sd15_light.bin): same as ip-adapter_sd15, but more compatible with text prompt
+- [ip-adapter-plus_sd15.bin](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-plus_sd15.bin): use patch image embeddings from OpenCLIP-ViT-H-14 as condition, closer to the reference image than ip-adapter_sd15
+- [ip-adapter-plus-face_sd15.bin](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-plus-face_sd15.bin): same as ip-adapter-plus_sd15, but use cropped face image as condition
+
+### IP-Adapter for SDXL 1.0
+- [ip-adapter_sdxl.bin](https://huggingface.co/h94/IP-Adapter/blob/main/sdxl_models/ip-adapter_sdxl.bin): use global image embedding from OpenCLIP-ViT-bigG-14 as condition
+- [ip-adapter_sdxl_vit-h.bin](https://huggingface.co/h94/IP-Adapter/blob/main/sdxl_models/ip-adapter_sdxl_vit-h.bin): same as ip-adapter_sdxl, but use OpenCLIP-ViT-H-14
+- [ip-adapter-plus_sdxl_vit-h.bin](https://huggingface.co/h94/IP-Adapter/blob/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin): use patch image embeddings from OpenCLIP-ViT-H-14 as condition, closer to the reference image than ip-adapter_xl and ip-adapter_sdxl_vit-h
+- [ip-adapter-plus-face_sdxl_vit-h.bin](https://huggingface.co/h94/IP-Adapter/blob/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.bin): same as ip-adapter-plus_sdxl_vit-h, but use cropped face image as condition
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LLaMA-2-7B-32K_finetunes_20250425_143346.csv_finetunes_20250425_143346.csv

@@ -0,0 +1,618 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+togethercomputer/LLaMA-2-7B-32K,"---
+license: llama2
+datasets:
+- togethercomputer/RedPajama-Data-1T
+- togethercomputer/RedPajama-Data-Instruct
+- EleutherAI/pile
+- togethercomputer/Long-Data-Collections
+language:
+- en
+library_name: transformers
+---
+
+# LLaMA-2-7B-32K
+
+## Model Description
+
+LLaMA-2-7B-32K is an open-source, long context language model developed by Together, fine-tuned from Meta's original Llama-2 7B model. 
+This model represents our efforts to contribute to the rapid progress of the open-source ecosystem for large language models. 
+The model has been extended to a context length of 32K with position interpolation, 
+allowing applications on multi-document QA, long text summarization, etc.
+
+## What's new?
+
+This model introduces several improvements and new features:
+
+1. **Extended Context:** The model has been trained to handle context lengths up to 32K, which is a significant improvement over the previous versions.
+
+2. **Pre-training and Instruction Tuning:** We have shared our data recipe, which consists of a mixture of pre-training and instruction tuning data.
+
+3. **Fine-tuning Examples:** We provide examples of how to fine-tune the model for specific applications, including book summarization and long context question and answering.
+
+4. **Software Support:** We have updated both the inference and training stack to allow efficient inference and fine-tuning for 32K context.
+
+## Model Architecture
+
+The model follows the architecture of Llama-2-7B and extends it to handle a longer context. It leverages the recently released FlashAttention-2 and a range of other optimizations to improve the speed and efficiency of inference and training.
+
+## Training and Fine-tuning
+
+The model has been trained using a mixture of pre-training and instruction tuning data. 
+- In the first training phase of continued pre-training, our data mixture contains 25% RedPajama Book, 25% RedPajama ArXiv (including abstracts), 25% other data from RedPajama, and 25% from the UL2 Oscar Data, which is a part of OIG (Open-Instruction-Generalist), asking the model to fill in missing chunks, or complete the text. 
+To enhance the long-context ability, we exclude data shorter than 2K word. The inclusion of UL2 Oscar Data is effective in compelling the model to read and utilize long-range context.
+- We then fine-tune the model to focus on its few shot capacity under long context, including 20% Natural Instructions (NI), 20% Public Pool of Prompts (P3), 20% the Pile. We decontaminated all data against HELM core scenarios . We teach the model to leverage the in-context examples by packing examples into one 32K-token sequence. To maintain the knowledge learned from the first piece of data, we incorporate 20% RedPajama-Data Book and 20% RedPajama-Data ArXiv.
+
+Next, we provide examples of how to fine-tune the model for specific applications. 
+The example datasets are placed in [togethercomputer/Long-Data-Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections)
+You can use the [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) to fine-tune your own 32K model over LLaMA-2-7B-32K.
+Please refer to [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) for step-by-step illustrations.
+
+1. Long Context QA.
+
+   We take as an example the multi-document question answering task from the paper “Lost in the Middle: How Language Models Use Long Contexts”. The input for the model consists of (i) a question that requires an answer and (ii) k documents, which are passages extracted from Wikipedia. Notably, only one of these documents contains the answer to the question, while the remaining k − 1 documents, termed as ""distractor"" documents, do not. To successfully perform this task, the model must identify and utilize the document containing the answer from its input context. 
+
+   With OCK, simply run the following command to fine-tune:
+   ```
+   bash training/finetune_llama-2-7b-32k-mqa.sh
+   ```
+
+2. Summarization.
+
+   Another example is BookSum, a unique dataset designed to address the challenges of long-form narrative summarization. This dataset features source documents from the literature domain, including novels, plays, and stories, and offers human-written, highly abstractive summaries. We here focus on chapter-level data.  BookSum poses a unique set of challenges, necessitating that the model comprehensively read through each chapter.
+
+   With OCK, simply run the following command to fine-tune:
+   ```
+   bash training/finetune_llama-2-7b-32k-booksum.sh
+   ```
+
+
+## Inference
+
+You can use the [Together API](https://together.ai/blog/api-announcement) to try out LLaMA-2-7B-32K for inference. 
+The updated inference stack allows for efficient inference.
+
+To run the model locally, we strongly recommend to install Flash Attention V2, which is necessary to obtain the best performance:
+```
+# Please update the path of `CUDA_HOME`
+export CUDA_HOME=/usr/local/cuda-11.8
+pip install transformers==4.31.0
+pip install sentencepiece
+pip install ninja
+pip install flash-attn --no-build-isolation
+pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
+```
+
+You can use this model directly from the Hugging Face Model Hub or fine-tune it on your own data using the OpenChatKit.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained(""togethercomputer/LLaMA-2-7B-32K"")
+model = AutoModelForCausalLM.from_pretrained(""togethercomputer/LLaMA-2-7B-32K"", trust_remote_code=True, torch_dtype=torch.float16)
+
+input_context = ""Your text here""
+input_ids = tokenizer.encode(input_context, return_tensors=""pt"")
+output = model.generate(input_ids, max_length=128, temperature=0.7)
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(output_text)
+```
+
+Alternatively, you can set `trust_remote_code=False` if you prefer not to use flash attention.
+
+
+## Limitations and Bias
+
+As with all language models, LLaMA-2-7B-32K may generate incorrect or biased content. It's important to keep this in mind when using the model.
+
+## Community
+
+Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)","{""id"": ""togethercomputer/LLaMA-2-7B-32K"", ""author"": ""togethercomputer"", ""sha"": ""46c24bb5aef59722fa7aa6d75e832afd1d64b980"", ""last_modified"": ""2024-03-28 01:14:07+00:00"", ""created_at"": ""2023-07-26 02:19:41+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 5855, ""downloads_all_time"": null, ""likes"": 538, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""llama"", ""text-generation"", ""en"", ""dataset:togethercomputer/RedPajama-Data-1T"", ""dataset:togethercomputer/RedPajama-Data-Instruct"", ""dataset:EleutherAI/pile"", ""dataset:togethercomputer/Long-Data-Collections"", ""license:llama2"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": 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Walid-Ahmed/Advanced_Text_Summarization, anantgupta129/LitGPT-Pythia-160M, gelnesr/Dyna-1, huggingface/InferenceSupport/discussions/new?title=togethercomputer/LLaMA-2-7B-32K&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Btogethercomputer%2FLLaMA-2-7B-32K%5D(%2Ftogethercomputer%2FLLaMA-2-7B-32K)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A",13
+flytech/togetherchat-dev-7b,"---
+license: llama2
+base_model: togethercomputer/LLaMA-2-7B-32K
+tags:
+- generated_from_trainer
+model-index:
+- name: togetherchat-dev-7b
+  results: []
+---
+
+# togetherchat-dev-7b
+
+This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) using 5000 examples and 3 datasets:
+
+platypus_dataset = load_dataset(""garage-bAInd/Open-Platypus"")
+codealpaca_dataset = load_dataset(""sahil2801/CodeAlpaca-20k"")
+evol_codealpaca_dataset = load_dataset(""theblackcat102/evol-codealpaca-v1"")
+
+
+## Model description
+
+Step	Training Loss
+---------------------
+    60	1.293000
+    120	0.673600
+    180	0.633200	
+    240	0.611600	
+    300	0.633000	
+    360	0.589500	
+    480	0.587600	
+    540	0.569000	
+    600	0.548700	
+    660	0.553100	
+    720	0.531500	
+    780	0.506400	
+    840	0.512500
+
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3
+","{""id"": ""flytech/togetherchat-dev-7b"", ""author"": ""flytech"", ""sha"": ""4d8b59990e1aa88a5e772b3d2190839055f57f06"", ""last_modified"": ""2023-09-09 19:46:21+00:00"", ""created_at"": ""2023-09-08 03:36:00+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 4, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""tensorboard"", ""safetensors"", ""llama"", ""text-generation"", ""generated_from_trainer"", ""custom_code"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""license:llama2"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nlicense: llama2\ntags:\n- 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+flytech/togetherchat-dev-7b-v2,"---
+license: llama2
+base_model: togethercomputer/LLaMA-2-7B-32K
+tags:
+- generated_from_trainer
+model-index:
+- name: togetherchat-dev-7b-v2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# togetherchat-dev-7b-v2
+
+This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) on 25000 entries for 3 epochs.
+
+## Model description
+
+Model can be used for text-to-code generation and for further fine-tuning,
+Colab notebook example (on free T4 GPU) soon!
+
+## Datasets used:
+
+- evol-codealpaca-80k - 10000 entries
+- codealpaca-20k - 10000 entries
+- open-platypus - 5000 entries
+
+## Intended uses & limitations
+
+Please remember that model may (and will) produce inaccurate informations, 
+you need to fine-tune it for your specific task.
+
+## Training and evaluation data
+
+See 'Metrics'
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 10
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 40
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3
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+flytech/Ruckus-7b-ALPHA,"---
+license: llama2
+base_model: togethercomputer/LLaMA-2-7B-32K
+tags:
+- generated_from_trainer
+model-index:
+- name: Ruckus-7b-ALPHA
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Ruckus-7b-ALPHA
+
+This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- num_epochs: 8
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3
+","{""id"": ""flytech/Ruckus-7b-ALPHA"", ""author"": ""flytech"", ""sha"": ""799472379c6cacd416ec34ad1a82e71baeaa6f60"", ""last_modified"": ""2023-09-19 03:34:09+00:00"", ""created_at"": ""2023-09-18 22:55:06+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 3, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""tensorboard"", ""safetensors"", ""llama"", ""text-generation"", ""generated_from_trainer"", ""custom_code"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""license:llama2"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nlicense: 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false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='events.out.tfevents.1695070720.ef7f542c87ff.675570.0', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='events.out.tfevents.1695070720.ef7f542c87ff.675570.1', size=None, blob_id=None, lfs=None)"", 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2048840176}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=flytech/Ruckus-7b-ALPHA&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bflytech%2FRuckus-7b-ALPHA%5D(%2Fflytech%2FRuckus-7b-ALPHA)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+flytech/Ruckus-7b-v17,"---
+license: llama2
+base_model: togethercomputer/LLaMA-2-7B-32K
+tags:
+- generated_from_trainer
+model-index:
+- name: Ruckus-7b-v17
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Ruckus-7b-v17
+
+This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- num_epochs: 11
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3
+","{""id"": ""flytech/Ruckus-7b-v17"", ""author"": ""flytech"", ""sha"": ""ba22ff8aa9f5d0840597d446357b7ef6696ee212"", ""last_modified"": ""2023-09-19 21:27:02+00:00"", ""created_at"": ""2023-09-19 14:15:29+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 8, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""tensorboard"", ""safetensors"", ""llama"", ""text-generation"", ""generated_from_trainer"", ""custom_code"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""license:llama2"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nlicense: llama2\ntags:\n- 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""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='events.out.tfevents.1695132941.ef7f542c87ff.925612.0', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='events.out.tfevents.1695132941.ef7f542c87ff.925612.1', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/optimizer.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/rng_state.pth', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/scheduler.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/trainer_state.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='last-checkpoint/training_args.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-09-19 21:27:02+00:00"", ""cardData"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nlicense: llama2\ntags:\n- generated_from_trainer\nmodel-index:\n- name: Ruckus-7b-v17\n  results: []"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""6509ad011aece923f2f2e2f3"", ""modelId"": ""flytech/Ruckus-7b-v17"", ""usedStorage"": 5158776983}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=flytech/Ruckus-7b-v17&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bflytech%2FRuckus-7b-v17%5D(%2Fflytech%2FRuckus-7b-v17)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: togethercomputer/LLaMA-2-7B-32K
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with hqq.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo togethercomputer/LLaMA-2-7B-32K installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install hqq
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+    from hqq.engine.hf import HQQModelForCausalLM
+ from hqq.models.hf.base import AutoHQQHFModel
+
+   try:
+     model = HQQModelForCausalLM.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed"", device_map='auto')
+    except: 
+     model = AutoHQQHFModel.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed"")
+   tokenizer = AutoTokenizer.from_pretrained(""togethercomputer/LLaMA-2-7B-32K"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model togethercomputer/LLaMA-2-7B-32K before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""ce1aab9cf5984e5a71562b30fcfd2e9f54ce52e3"", ""last_modified"": ""2024-08-02 16:17:58+00:00"", ""created_at"": ""2024-06-24 11:32:47+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 2, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""llama"", ""text-generation"", ""pruna-ai"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""eos_token"": ""</s>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='qmodel.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-08-02 16:17:58+00:00"", ""cardData"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""6679595f2c09cbf312b8c442"", ""modelId"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed"", ""usedStorage"": 2296481611}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed%5D(%2FPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-2bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: togethercomputer/LLaMA-2-7B-32K
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with hqq.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo togethercomputer/LLaMA-2-7B-32K installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install hqq
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+    from hqq.engine.hf import HQQModelForCausalLM
+ from hqq.models.hf.base import AutoHQQHFModel
+
+   try:
+     model = HQQModelForCausalLM.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed"", device_map='auto')
+    except: 
+     model = AutoHQQHFModel.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed"")
+   tokenizer = AutoTokenizer.from_pretrained(""togethercomputer/LLaMA-2-7B-32K"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model togethercomputer/LLaMA-2-7B-32K before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""105cd84804933a225910655cd35bfe27745dc13e"", ""last_modified"": ""2024-08-02 16:18:00+00:00"", ""created_at"": ""2024-06-24 11:33:42+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 2, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""llama"", ""text-generation"", ""pruna-ai"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""eos_token"": ""</s>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='qmodel.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-08-02 16:18:00+00:00"", ""cardData"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""66795996335cad277cd4219d"", ""modelId"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed"", ""usedStorage"": 1486980939}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed%5D(%2FPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-1bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: togethercomputer/LLaMA-2-7B-32K
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with hqq.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo togethercomputer/LLaMA-2-7B-32K installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install hqq
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+    from hqq.engine.hf import HQQModelForCausalLM
+ from hqq.models.hf.base import AutoHQQHFModel
+
+   try:
+     model = HQQModelForCausalLM.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed"", device_map='auto')
+    except: 
+     model = AutoHQQHFModel.from_quantized(""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed"")
+   tokenizer = AutoTokenizer.from_pretrained(""togethercomputer/LLaMA-2-7B-32K"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model togethercomputer/LLaMA-2-7B-32K before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""fee3782a86c9903bd549f76488546e2c9a68201a"", ""last_modified"": ""2024-08-02 16:18:01+00:00"", ""created_at"": ""2024-06-24 11:33:45+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 3, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""llama"", ""text-generation"", ""pruna-ai"", ""base_model:togethercomputer/LLaMA-2-7B-32K"", ""base_model:finetune:togethercomputer/LLaMA-2-7B-32K"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""eos_token"": ""</s>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='qmodel.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-08-02 16:18:01+00:00"", ""cardData"": ""base_model: togethercomputer/LLaMA-2-7B-32K\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""66795999892c0a171612dfe1"", ""modelId"": ""PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed"", ""usedStorage"": 3915482955}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/togethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed%5D(%2FPrunaAI%2Ftogethercomputer-LLaMA-2-7B-32K-HQQ-4bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Llama-2-70B-Chat-GGML_finetunes_20250427_003734.csv_finetunes_20250427_003734.csv

@@ -0,0 +1,303 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+TheBloke/Llama-2-70B-Chat-GGML,"---
+language:
+- en
+license: other
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-2
+model_name: Llama 2 70B Chat
+inference: false
+model_creator: Meta Llama 2
+model_link: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+model_type: llama
+pipeline_tag: text-generation
+quantized_by: TheBloke
+base_model: meta-llama/Llama-2-70b-chat-hf
+---
+
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+<img src=""https://i.imgur.com/EBdldam.jpg"" alt=""TheBlokeAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+</div>
+<div style=""display: flex; justify-content: space-between; width: 100%;"">
+    <div style=""display: flex; flex-direction: column; align-items: flex-start;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://discord.gg/theblokeai"">Chat & support: TheBloke's Discord server</a></p>
+    </div>
+    <div style=""display: flex; flex-direction: column; align-items: flex-end;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://www.patreon.com/TheBlokeAI"">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<div style=""text-align:center; margin-top: 0em; margin-bottom: 0em""><p style=""margin-top: 0.25em; margin-bottom: 0em;"">TheBloke's LLM work is generously supported by a grant from <a href=""https://a16z.com"">andreessen horowitz (a16z)</a></p></div>
+<hr style=""margin-top: 1.0em; margin-bottom: 1.0em;"">
+<!-- header end -->
+
+# Llama 2 70B Chat - GGML
+- Model creator: [Meta Llama 2](https://huggingface.co/meta-llama)
+- Original model: [Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
+
+## Description
+
+This repo contains GGML format model files for [Meta Llama 2's Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf).
+
+### Important note regarding GGML files.
+
+The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.
+
+Please use the GGUF models instead.
+
+### About GGML
+
+GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration:
+* [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), version 1.37 and later. A powerful GGML web UI, especially good for story telling.
+* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration for both Windows and macOS. Use 0.1.11 or later for macOS GPU acceleration with 70B models.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.
+* [ctransformers](https://github.com/marella/ctransformers), version 0.2.15 and later. A Python library with LangChain support, and OpenAI-compatible API server.
+
+## Repositories available
+
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-70B-chat-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML)
+* [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
+
+## Prompt template: Llama-2-Chat
+
+```
+[INST] <<SYS>>
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+<</SYS>>
+{prompt}[/INST]
+
+```
+
+<!-- compatibility_ggml start -->
+## Compatibility
+
+### Works with llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) until August 21st, 2023
+
+Will not work with `llama.cpp` after commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa).
+
+For compatibility with latest llama.cpp, please use GGUF files instead.
+
+Or one of the other tools and libraries listed above.
+
+To use in llama.cpp, you must add `-gqa 8` argument.
+
+For other UIs and libraries, please check the docs.
+
+## Explanation of the new k-quant methods
+<details>
+  <summary>Click to see details</summary>
+
+The new methods available are:
+* GGML_TYPE_Q2_K - ""type-1"" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
+* GGML_TYPE_Q3_K - ""type-0"" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
+* GGML_TYPE_Q4_K - ""type-1"" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
+* GGML_TYPE_Q5_K - ""type-1"" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
+* GGML_TYPE_Q6_K - ""type-0"" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
+* GGML_TYPE_Q8_K - ""type-0"" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
+
+Refer to the Provided Files table below to see what files use which methods, and how.
+</details>
+<!-- compatibility_ggml end -->
+
+## Provided files
+
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| llama-2-70b-chat.ggmlv3.q2_K.bin | q2_K | 2 | 28.59 GB| 31.09 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
+| llama-2-70b-chat.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 29.75 GB| 32.25 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| llama-2-70b-chat.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 33.04 GB| 35.54 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
+| llama-2-70b-chat.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 36.15 GB| 38.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
+| llama-2-70b-chat.ggmlv3.q4_0.bin | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. |
+| llama-2-70b-chat.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 38.87 GB| 41.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| llama-2-70b-chat.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 41.38 GB| 43.88 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
+| llama-2-70b-chat.ggmlv3.q4_1.bin | q4_1 | 4 | 43.17 GB| 45.67 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
+| llama-2-70b-chat.ggmlv3.q5_0.bin | q5_0 | 5 | 47.46 GB| 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
+| llama-2-70b-chat.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 47.46 GB| 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| llama-2-70b-chat.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 48.75 GB| 51.25 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
+
+**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
+
+## How to run in `llama.cpp`
+
+Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.
+
+For compatibility with latest llama.cpp, please use GGUF files instead.
+
+I use the following command line; adjust for your tastes and needs:
+
+```
+./main -t 10 -ngl 40 -gqa 8 -m llama-2-70b-chat.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p ""[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\nWrite a story about llamas[/INST]""
+```
+Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. If you are fully offloading the model to GPU, use `-t 1`
+
+Change `-ngl 40` to the number of GPU layers you have VRAM for. Use `-ngl 100` to offload all layers to VRAM - if you have a 48GB card, or 2 x 24GB, or similar.  Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.
+
+If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
+
+Remember the `-gqa 8` argument, required for Llama 70B models.
+
+Change `-c 4096` to the desired sequence length for this model. For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context.
+
+For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+## How to run in `text-generation-webui`
+
+Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
+
+<!-- footer start -->
+<!-- 200823 -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Aemon Algiz.
+
+**Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser
+
+
+Thank you to all my generous patrons and donaters!
+
+And thank you again to a16z for their generous grant.
+
+<!-- footer end -->
+
+# Original model card: Meta Llama 2's Llama 2 70B Chat
+
+# **Llama 2**
+Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
+
+## Model Details
+*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
+
+Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
+
+**Model Developers** Meta
+
+**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
+
+**Input** Models input text only.
+
+**Output** Models generate text only.
+
+**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
+
+
+||Training Data|Params|Content Length|GQA|Tokens|LR|
+|---|---|---|---|---|---|---|
+|Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
+|Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
+|Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
+
+*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models -  70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
+
+**Model Dates** Llama 2 was trained between January 2023 and July 2023.
+
+**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
+
+**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
+
+**Research Paper** [""Llama-2: Open Foundation and Fine-tuned Chat Models""](arxiv.org/abs/2307.09288)
+
+## Intended Use
+**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
+
+**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
+
+## Hardware and Software
+**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
+
+**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
+
+||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
+|---|---|---|---|
+|Llama 2 7B|184320|400|31.22|
+|Llama 2 13B|368640|400|62.44|
+|Llama 2 70B|1720320|400|291.42|
+|Total|3311616||539.00|
+
+**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
+
+## Training Data
+**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
+
+**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
+
+## Evaluation Results
+
+In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
+
+|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
+|---|---|---|---|---|---|---|---|---|---|
+|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
+|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
+|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
+|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
+|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
+|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
+|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
+
+**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
+
+|||TruthfulQA|Toxigen|
+|---|---|---|---|
+|Llama 1|7B|27.42|23.00|
+|Llama 1|13B|41.74|23.08|
+|Llama 1|33B|44.19|22.57|
+|Llama 1|65B|48.71|21.77|
+|Llama 2|7B|33.29|**21.25**|
+|Llama 2|13B|41.86|26.10|
+|Llama 2|70B|**50.18**|24.60|
+
+**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
+
+
+|||TruthfulQA|Toxigen|
+|---|---|---|---|
+|Llama-2-Chat|7B|57.04|**0.00**|
+|Llama-2-Chat|13B|62.18|**0.00**|
+|Llama-2-Chat|70B|**64.14**|0.01|
+
+**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
+
+## Ethical Considerations and Limitations
+Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
+
+Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
+
+## Reporting Issues
+Please report any software “bug,” or other problems with the models through one of the following means:
+- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+
+## Llama Model Index
+|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
+|---|---|---|---|---|
+|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
+|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
+|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
+","{""id"": ""TheBloke/Llama-2-70B-Chat-GGML"", ""author"": ""TheBloke"", ""sha"": ""d59cdcb92017737098edc4b293e4f0724021a475"", ""last_modified"": ""2023-09-27 13:00:24+00:00"", ""created_at"": ""2023-07-23 13:16:43+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 14, ""downloads_all_time"": null, ""likes"": 161, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""llama"", ""facebook"", ""meta"", ""pytorch"", ""llama-2"", ""text-generation"", ""en"", ""arxiv:2307.09288"", ""base_model:meta-llama/Llama-2-70b-chat-hf"", ""base_model:finetune:meta-llama/Llama-2-70b-chat-hf"", ""license:other"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: meta-llama/Llama-2-70b-chat-hf\nlanguage:\n- en\nlicense: other\nmodel_name: Llama 2 70B Chat\npipeline_tag: text-generation\ntags:\n- facebook\n- meta\n- pytorch\n- llama\n- llama-2\ninference: false\nmodel_creator: Meta Llama 2\nmodel_link: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf\nmodel_type: llama\nquantized_by: TheBloke"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""model_type"": ""llama""}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='Notice', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='USE_POLICY.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q2_K.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q3_K_L.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q3_K_M.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q3_K_S.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q4_0.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q4_1.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q4_K_M.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q4_K_S.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q5_0.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q5_K_M.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q5_K_S.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q6_K.z01', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q6_K.zip', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q8_0.z01', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='llama-2-70b-chat.ggmlv3.q8_0.zip', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-09-27 13:00:24+00:00"", ""cardData"": ""base_model: meta-llama/Llama-2-70b-chat-hf\nlanguage:\n- en\nlicense: other\nmodel_name: Llama 2 70B Chat\npipeline_tag: text-generation\ntags:\n- facebook\n- meta\n- pytorch\n- llama\n- llama-2\ninference: false\nmodel_creator: Meta Llama 2\nmodel_link: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf\nmodel_type: llama\nquantized_by: TheBloke"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""64bd283bae436c88130e6b5e"", ""modelId"": ""TheBloke/Llama-2-70B-Chat-GGML"", ""usedStorage"": 718454942698}",0,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=TheBloke/Llama-2-70B-Chat-GGML&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BTheBloke%2FLlama-2-70B-Chat-GGML%5D(%2FTheBloke%2FLlama-2-70B-Chat-GGML)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Llama-3-Refueled_finetunes_20250426_221535.csv_finetunes_20250426_221535.csv

@@ -0,0 +1,135 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+refuelai/Llama-3-Refueled,"---
+license: cc-by-nc-4.0
+language:
+- en
+library_name: transformers
+tags:
+- data labeling
+---
+<div style=""width: auto; margin-left: auto; margin-right: auto; background-color:black"">
+<img src=""https://assets-global.website-files.com/6423879a8f63c1bb18d74bfa/648818d56d04c3bdf36d71ab_Refuel_rev8-01_ts-p-1600.png"" alt=""Refuel.ai"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+</div>
+
+## Model Details
+
+RefuelLLM-2-small, aka Llama-3-Refueled, is a Llama3-8B base model instruction tuned on a corpus of 2750+ datasets, spanning tasks such as classification, reading comprehension, structured attribute extraction and entity resolution. We're excited to open-source the model for the community to build on top of. 
+
+* More details about [RefuelLLM-2 family of models](https://www.refuel.ai/blog-posts/announcing-refuel-llm-2)
+* You can also try out the models in our [LLM playground](https://labs.refuel.ai/playground)
+
+**Model developers** - Refuel AI
+
+**Input** - Text only.
+
+**Output** - Text only.
+
+**Architecture** - Llama-3-Refueled is built on top of Llama-3-8B-instruct which is an auto-regressive language model that uses an optimized transformer architecture. 
+
+**Release Date** - May 8, 2024.
+
+**License** - [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en)
+
+## How to use
+
+This repository contains weights for Llama-3-Refueled that are compatible for use with HuggingFace. See the snippet below for usage with Transformers:
+
+```python
+>>> import torch
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
+>>> model_id = ""refuelai/Llama-3-Refueled""
+>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
+>>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map=""auto"")
+
+>>> messages = [{""role"": ""user"", ""content"": ""Is this comment toxic or non-toxic: RefuelLLM is the new way to label text data!""}]
+
+>>> inputs = tokenizer.apply_chat_template(messages, return_tensors=""pt"", add_generation_prompt=True).to(""cuda"")
+
+>>> outputs = model.generate(inputs, max_new_tokens=20)
+>>> print(tokenizer.decode(outputs[0]))
+```
+
+## Training Data
+
+The model was both trained on over 4 Billion tokens, spanning 2750+ NLP tasks. Our training collection consists majorly of:
+1. Human annotated datasets like Flan, Task Source, and the Aya collection 
+2. Synthetic datasets like OpenOrca, OpenHermes and WizardLM
+3. Proprietary datasets developed or licensed by Refuel AI
+
+## Benchmarks 
+
+In this section, we report the results for Refuel models on our benchmark of labeling tasks. For details on the methodology see [here](https://refuel.ai/blog-posts/announcing-refuel-llm-2).
+
+<table>
+<tr></tr>
+<tr><th>Provider</th><th>Model</th><th colspan=""4"" style=""text-align: center"">LLM Output Quality (by task type)</tr>
+<tr><td></td><td></td><td>Overall</td><td>Classification</td><td>Reading Comprehension</td><td>Structure Extraction</td><td>Entity Matching</td><td></td></tr>
+<tr><td>Refuel</td><td>RefuelLLM-2</td><td>83.82%</td><td>84.94%</td><td>76.03%</td><td>88.16%</td><td>92.00%</td><td></td></tr>
+<tr><td>OpenAI</td><td>GPT-4-Turbo</td><td>80.88%</td><td>81.77%</td><td>72.08%</td><td>84.79%</td><td>97.20%</td><td></td></tr>
+<tr><td>Refuel</td><td>RefuelLLM-2-small (Llama-3-Refueled)</td><td>79.67%</td><td>81.72%</td><td>70.04%</td><td>84.28%</td><td>92.00%</td><td></td></tr>
+<tr><td>Anthropic</td><td>Claude-3-Opus</td><td>79.19%</td><td>82.49%</td><td>67.30%</td><td>88.25%</td><td>94.96%</td><td></td></tr>
+<tr><td>Meta</td><td>Llama3-70B-Instruct</td><td>78.20%</td><td>79.38%</td><td>66.03%</td><td>85.96%</td><td>94.13%</td><td></td></tr>
+<tr><td>Google</td><td>Gemini-1.5-Pro</td><td>74.59%</td><td>73.52%</td><td>60.67%</td><td>84.27%</td><td>98.48%</td><td></td></tr>
+<tr><td>Mistral</td><td>Mixtral-8x7B-Instruct</td><td>62.87%</td><td>79.11%</td><td>45.56%</td><td>47.08%</td><td>86.52%</td><td></td></tr>
+<tr><td>Anthropic</td><td>Claude-3-Sonnet</td><td>70.99%</td><td>79.91%</td><td>45.44%</td><td>78.10%</td><td>96.34%</td><td></td></tr>
+<tr><td>Anthropic</td><td>Claude-3-Haiku</td><td>69.23%</td><td>77.27%</td><td>50.19%</td><td>84.97%</td><td>54.08%</td><td></td></tr>
+<tr><td>OpenAI</td><td>GPT-3.5-Turbo</td><td>68.13%</td><td>74.39%</td><td>53.21%</td><td>69.40%</td><td>80.41%</td><td></td></tr>
+<tr><td>Meta</td><td>Llama3-8B-Instruct</td><td>62.30%</td><td>68.52%</td><td>49.16%</td><td>65.09%</td><td>63.61%</td><td></td></tr>
+</table>
+
+
+## Limitations
+
+The Llama-3-Refueled does not have any moderation mechanisms. We're looking forward to engaging with the community 
+on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.","{""id"": ""refuelai/Llama-3-Refueled"", ""author"": ""refuelai"", ""sha"": ""ff6d1c3ba37b31d4af421951c2300f2256fb3691"", ""last_modified"": ""2024-05-09 20:42:29+00:00"", ""created_at"": ""2024-05-03 05:16:26+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 317, ""downloads_all_time"": null, ""likes"": 190, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""data labeling"", ""conversational"", ""en"", ""license:cc-by-nc-4.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\nlibrary_name: transformers\nlicense: 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""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='trainer_state.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='zero_to_fp32.py', size=None, blob_id=None, lfs=None)""], ""spaces"": [""featherless-ai/try-this-model"", ""Darok/Featherless-Feud"", ""emekaboris/try-this-model"", ""SC999/NV_Nemotron"", ""JackHoltone/try-this-model"", ""k11112/try-this-model""], ""safetensors"": {""parameters"": {""BF16"": 8030261248}, ""total"": 8030261248}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-05-09 20:42:29+00:00"", ""cardData"": ""language:\n- en\nlibrary_name: transformers\nlicense: cc-by-nc-4.0\ntags:\n- data labeling"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": 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huggingface/InferenceSupport/discussions/new?title=refuelai/Llama-3-Refueled&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Brefuelai%2FLlama-3-Refueled%5D(%2Frefuelai%2FLlama-3-Refueled)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, k11112/try-this-model",7
+QuietImpostor/Llama-3-Refueled-Pruned,"---
+base_model:
+- refuelai/Llama-3-Refueled
+library_name: transformers
+tags:
+- mergekit
+- merge
+license: llama3
+datasets:
+- yahma/alpaca-cleaned
+language:
+- en
+---
+### Pruning Details
+
+This is a prune of [Llama 3 Refueled](https://www.huggingface.co/refuelai/llama-3-refueled) using [mergekit](https://github.com/cg123/mergekit) and [PruneMe](https://www.github.com/arcee-ai/PruneMe)
+The model is semi-tested, but still needs some debugging, namely with converting to GGUF, though I am working on that.
+
+Note: the [dataset](https://www.huggingface.co/yahma/alpaca-cleaned) was used for evaluating what layers should be pruned. This model was **NOT** finetuned.
+
+### Performance
+After only 1 test because of lack of compute and for stupid long inference times on my 3060ti (8GB), it does show some interesting results.
+Here's the response after being prompted ""Hi!"" using the [example from Meta](https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3).
+
+```model_response
+ vel tips and recommendations.user
+Hi!assistant
+Hi! I can help you find the best travel tips and recommendations for your next trip. Where you most interested to travel and what kind of activities you most to to the 9e sure, we can start and letiing 10e 11e 12e 13e 14e 15e 16e 17e 18e 19e 20e 21e 23e 24e 5e 6e 7e 8e 9e 10e 11e 12e 13e 14e 15e
+```
+
+Even without finetuning, the model still exhibits some extent of instruction following.
+And fine-tuning is a WIP and I will update this when it's ready.
+Finetuning is no longer in progress due to issues with unsloth. However, I am working on a project that will hopefully make pruning models easier.
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+slices:
+  - sources:
+      - model: refuelai/Llama-3-Refueled
+        layer_range: [0, 19]
+  - sources:
+      - model: refuelai/Llama-3-Refueled
+        layer_range: [29, 32]
+
+merge_method: passthrough
+dtype: bfloat16
+```","{""id"": ""QuietImpostor/Llama-3-Refueled-Pruned"", ""author"": ""QuietImpostor"", ""sha"": ""997186e4683d6648413cc6f5b664d9e30c558627"", ""last_modified"": ""2024-05-28 17:31:08+00:00"", ""created_at"": ""2024-05-21 19:26:24+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 9, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""mergekit"", ""merge"", ""conversational"", ""en"", ""dataset:yahma/alpaca-cleaned"", ""base_model:refuelai/Llama-3-Refueled"", ""base_model:finetune:refuelai/Llama-3-Refueled"", ""license:llama3"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- refuelai/Llama-3-Refueled\ndatasets:\n- yahma/alpaca-cleaned\nlanguage:\n- en\nlibrary_name: transformers\nlicense: llama3\ntags:\n- mergekit\n- merge"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<|begin_of_text|>"", ""chat_template"": ""{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|end_of_text|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='mergekit_config.yml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00002.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00002.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""BF16"": 5849141248}, ""total"": 5849141248}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-05-28 17:31:08+00:00"", ""cardData"": ""base_model:\n- refuelai/Llama-3-Refueled\ndatasets:\n- yahma/alpaca-cleaned\nlanguage:\n- en\nlibrary_name: transformers\nlicense: llama3\ntags:\n- mergekit\n- merge"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""664cf560567cb471efe736e0"", ""modelId"": ""QuietImpostor/Llama-3-Refueled-Pruned"", ""usedStorage"": 11698305888}",1,,0,,0,https://huggingface.co/mradermacher/Llama-3-Refueled-Pruned-GGUF,1,,0,huggingface/InferenceSupport/discussions/new?title=QuietImpostor/Llama-3-Refueled-Pruned&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BQuietImpostor%2FLlama-3-Refueled-Pruned%5D(%2FQuietImpostor%2FLlama-3-Refueled-Pruned)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Llama-3_1-Nemotron-51B-Instruct_finetunes_20250426_212347.csv_finetunes_20250426_212347.csv

@@ -0,0 +1,216 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+nvidia/Llama-3_1-Nemotron-51B-Instruct,"---
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - nvidia
+  - llama-3
+  - pytorch
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+---
+
+# Llama-3_1-Nemotron-51B-instruct
+
+
+
+## Model Overview
+Llama-3_1-Nemotron-51B-instruct is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to price, providing great ‘quality-per-dollar’. Using a novel Neural Architecture Search (NAS) approach we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H100-80GB). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. This model is ready for commercial use.
+
+
+## License
+Your use of this model is governed by the [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
+Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.
+
+## How was the model developed
+
+Llama-3_1-Nemotron-51B-instruct is a large language model (LLM) which is a derivative of Llama-3.1-70B-instruct (AKA the reference model). We utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use-cases.
+The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz-V1.2 and Dolma.
+
+Links to [NIM](https://build.nvidia.com/nvidia/llama-3_1-nemotron-51b-instruct), [blog](https://developer.nvidia.com/blog/advancing-the-accuracy-efficiency-frontier-with-llama-3-1-nemotron-51b/) and [huggingface](https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct)
+
+
+
+This results in a final model that is aligned for human chat preferences.
+
+**Model Developers:** NVIDIA
+
+**Model Input:** Text only
+
+**Model Output:** Text only
+
+**Model Dates:** Llama-3_1-Nemotron-51B-instruct was trained between August and September 2024
+
+**Data Freshness:** The pretraining data has a cutoff of 2023
+
+**Sequence Length Used During Distillation:** 8192
+
+
+## Quick Start
+Our code requires the `transformers` package version to be 4.44.2 or higher
+
+See the snippet below for usage with transformers:
+```python
+import torch
+import transformers
+
+model_id = ""nvidia/Llama-3_1-Nemotron-51B-Instruct""
+model_kwargs = {""torch_dtype"": torch.bfloat16, ""trust_remote_code"": True, ""device_map"": ""auto""}
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+pipeline = transformers.pipeline(
+    ""text-generation"", 
+    model=model_id, 
+    tokenizer=tokenizer, 
+    max_new_tokens=20, 
+    **model_kwargs
+)
+print(pipeline([{""role"": ""user"", ""content"": ""Hey how are you?""}]))
+```
+
+
+
+## Required Hardware
+
+FP8 Inference (recommended):
+- 1x H100-80GB GPU
+
+BF16 Inference:
+- 2x H100-80GB GPUs
+- 2x A100-80GB GPUs
+
+
+## Model Architecture
+The model is a derivative of Llama-3.1-70B, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following: 
+* Variable Grouped Query Attention (VGQA) - each block can have a different number of KV (keys and values) heads, ranging from 1 to Llama’s typical 8.
+* Skip attention - in some blocks the attention is skipped entirely, or replaced with a single linear layer.
+* Variable FFN - the expansion/compression ratio in the FFN layer is different between blocks. 
+
+
+**Architecture Type:** Transformer Decoder (auto-regressive language model)
+
+## Software Integration
+**Runtime Engine(s):** 
+* NeMo 24.05 <br>
+
+
+**Supported Hardware Architecture Compatibility:** NVIDIA H100, A100 80GB (BF16 quantization).
+
+**[Preferred/Supported] Operating System(s):** <br>
+* Linux <br>
+
+## Intended use
+
+Llama-3_1-Nemotron-51B-Instruct  is a general purpose chat model intended to be used in English and coding languages. Other non-English languages are also supported.
+
+## Evaluation Results
+
+**Data Collection Method by dataset** <br>
+* Automated <br>
+
+
+### MT-Bench
+
+Evaluated using select datasets from the [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://arxiv.org/pdf/2306.05685v4)
+MT-bench - 8.99
+
+
+### MMLU
+
+Evaluated using the Multi-task Language Understanding benchmarks as introduced in [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300)
+
+|MMLU (5-shot) |
+| :----------------- |
+| 80.2%  | 
+
+### GSM8K
+
+Evaluated using the Grade School Math 8K (GSM8K) benchmark as introduced in [Training Verifiers to Solve Math Word Problems](https://arxiv.org/pdf/2110.14168v2)
+
+|GSM8K (5-shot) |
+| :----------------- |
+| 91.43%  | 
+
+### Winogrande
+
+|Winogrande (5-shot) |
+| :----------------- |
+| 84.53%  | 
+
+### Arc-C
+
+|Arc challenge (25-shot) |
+| :----------------- |
+| 69.20%  | 
+
+### Hellaswag
+
+|Hellaswag (10-shot) |
+| :----------------- |
+| 85.58%  | 
+
+### Truthful QA
+
+|TruthfulQA (0-shot) |
+| :----------------- |
+| 58.63%%  | 
+
+## Limitations
+
+The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
+
+The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs.
+
+## Adversarial Testing and Red Teaming Efforts 
+
+The Llama-3_1-Nemotron-51B-instruct model underwent extensive safety evaluation including adversarial testing via three distinct methods: 
+* [Garak](https://docs.garak.ai/garak), is an automated LLM vulnerability scanner that probes for common weaknesses, including prompt injection and data leakage. 
+* [AEGIS](https://arxiv.org/pdf/2404.05993), is a content safety evaluation dataset and LLM based content safety classifier model, that adheres to a broad taxonomy of 13 categories of critical risks in human-LLM interactions.
+* Human Content Red Teaming leveraging human interaction and evaluation of the models' responses.
+
+
+## Inference
+**Engine:** Tensor(RT) <br>
+**Test Hardware** H100-80GB <br>
+
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","{""id"": ""nvidia/Llama-3_1-Nemotron-51B-Instruct"", ""author"": ""nvidia"", ""sha"": ""2e9bdbfa8e1ab73ce98f5960143cf927192d59e7"", ""last_modified"": ""2025-04-08 17:43:57+00:00"", ""created_at"": ""2024-09-22 07:24:59+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 107666, ""downloads_all_time"": null, ""likes"": 206, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""nemotron-nas"", ""text-generation"", ""nvidia"", ""llama-3"", ""pytorch"", ""conversational"", ""custom_code"", ""en"", ""arxiv:2306.05685"", ""arxiv:2009.03300"", ""arxiv:2110.14168"", ""arxiv:2404.05993"", ""license:other"", ""autotrain_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\nlibrary_name: transformers\nlicense: other\nlicense_name: nvidia-open-model-license\nlicense_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf\npipeline_tag: text-generation\ntags:\n- nvidia\n- llama-3\n- pytorch"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""DeciLMForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_decilm.DeciLMConfig"", ""AutoModelForCausalLM"": ""modeling_decilm.DeciLMForCausalLM""}, ""model_type"": ""nemotron-nas"", ""tokenizer_config"": {""bos_token"": ""<|begin_of_text|>"", ""chat_template"": ""{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \""26 Jul 2024\"" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \""\"" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \""<|start_header_id|>system<|end_header_id|>\\n\\n\"" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \""Environment: ipython\\n\"" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \""Tools: \"" + builtin_tools | reject('equalto', 'code_interpreter') | join(\"", \"") + \""\\n\\n\""}}\n{%- endif %}\n{{- \""Cutting Knowledge Date: December 2023\\n\"" }}\n{{- \""Today Date: \"" + date_string + \""\\n\\n\"" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \""You have access to the following functions. 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huggingface/InferenceSupport/discussions/new?title=nvidia/Llama-3_1-Nemotron-51B-Instruct&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bnvidia%2FLlama-3_1-Nemotron-51B-Instruct%5D(%2Fnvidia%2FLlama-3_1-Nemotron-51B-Instruct)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, ruslanmv/convert_to_gguf, totolook/Quant",8
+huihui-ai/Llama-3_1-Nemotron-51B-Instruct-abliterated,"---
+base_model:
+  - nvidia/Llama-3_1-Nemotron-51B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - nvidia
+  - llama-3
+  - pytorch
+  - abliterated
+  - uncensored
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+---
+
+# huihui-ai/Llama-3_1-Nemotron-51B-Instruct-abliterated
+
+This is an uncensored version of [nvidia/Llama-3_1-Nemotron-51B-Instruct](https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).  
+This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.    
+
+### Donation
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin:
+```
+  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+```
+","{""id"": ""huihui-ai/Llama-3_1-Nemotron-51B-Instruct-abliterated"", ""author"": ""huihui-ai"", ""sha"": ""a684254b721363f03eb280fd71b9e6905a0c1653"", ""last_modified"": ""2025-02-12 20:05:59+00:00"", ""created_at"": ""2025-02-11 16:11:27+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 21, ""downloads_all_time"": null, ""likes"": 4, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""nemotron-nas"", ""text-generation"", ""nvidia"", ""llama-3"", ""pytorch"", ""abliterated"", ""uncensored"", ""conversational"", ""custom_code"", ""en"", ""base_model:nvidia/Llama-3_1-Nemotron-51B-Instruct"", ""base_model:finetune:nvidia/Llama-3_1-Nemotron-51B-Instruct"", ""license:other"", ""autotrain_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- nvidia/Llama-3_1-Nemotron-51B-Instruct\nlanguage:\n- en\nlibrary_name: transformers\nlicense: other\nlicense_name: nvidia-open-model-license\nlicense_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf\npipeline_tag: text-generation\ntags:\n- nvidia\n- llama-3\n- pytorch\n- abliterated\n- uncensored"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""DeciLMForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_decilm.DeciLMConfig"", ""AutoModelForCausalLM"": ""modeling_decilm.DeciLMForCausalLM""}, ""model_type"": ""nemotron-nas"", ""tokenizer_config"": {""bos_token"": ""<|begin_of_text|>"", ""chat_template"": ""{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \""26 Jul 2024\"" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \""\"" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \""<|start_header_id|>system<|end_header_id|>\\n\\n\"" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \""Environment: ipython\\n\"" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \""Tools: \"" + builtin_tools | reject('equalto', 'code_interpreter') | join(\"", \"") + \""\\n\\n\""}}\n{%- endif %}\n{{- \""Cutting Knowledge Date: December 2023\\n\"" }}\n{{- \""Today Date: \"" + date_string + \""\\n\\n\"" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \""You have access to the following functions. To call a function, please respond with JSON for a function call.\"" }}\n    {{- 'Respond in the format {\""name\"": function name, \""parameters\"": dictionary of argument name and its value}.' }}\n    {{- \""Do not use variables.\\n\\n\"" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \""\\n\\n\"" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \""<|eot_id|>\"" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\""Cannot put tools in the first user message when there's no first user message!\"") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \""Given 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tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \""<|python_tag|>\"" + tool_call.name + \"".call(\"" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\""' + arg_val + '\""' }}\n                {%- if not loop.last %}\n                    {{- \"", \"" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \"")\"" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\""name\"": \""' + tool_call.name + '\"", ' }}\n            {{- '\""parameters\"": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \""}\"" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \""<|eom_id|>\"" }}\n        {%- else %}\n            {{- \""<|eot_id|>\"" }}\n        {%- endif %}\n    {%- elif message.role == \""tool\"" or message.role == \""ipython\"" %}\n        {{- \""<|start_header_id|>ipython<|end_header_id|>\\n\\n\"" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \""<|eot_id|>\"" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"", ""eos_token"": ""<|eot_id|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""modeling_decilm.DeciLMForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='NOTICE', size=None, 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MiniMax-VL-01_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

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Molmo-72B-0924_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,211 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+allenai/Molmo-72B-0924,"---
+license: apache-2.0
+language:
+- en
+base_model:
+- openai/clip-vit-large-patch14-336
+- Qwen/Qwen2-72B
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- olmo
+- molmo
+- pixmo
+library_name: transformers
+---
+
+<img src=""molmo_logo.png"" alt=""Logo for the Molmo Project"" style=""width: auto; height: 50px;"">
+
+# Molmo 72B
+
+Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. You can find all models in the Molmo family [here](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19).
+**Learn more** about the Molmo family [in our announcement blog post](https://molmo.allenai.org/blog) or the [paper](https://huggingface.co/papers/2409.17146).
+
+Molmo 72B is based on [Qwen2-72B](https://huggingface.co/Qwen/Qwen2-72B) and uses [OpenAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) as vision backbone. 
+Molmo-72B achieves the highest academic benchmark score and ranks second on human evaluation, just slightly behind GPT-4o.
+
+This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
+
+[**Sign up here**](https://docs.google.com/forms/d/e/1FAIpQLSdML1MhNNBDsCHpgWG65Oydg2SjZzVasyqlP08nBrWjZp_c7A/viewform) to be the first to know when artifacts are released.
+
+Quick links:
+- 💬 [Demo](https://molmo.allenai.org/)
+- 📂 [All Models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19)
+- 📃 [Paper](https://molmo.allenai.org/paper.pdf)
+- 🎥 [Blog with Videos](https://molmo.allenai.org/blog)
+
+## Quick Start
+
+To run Molmo, first install dependencies:
+
+```bash
+pip install einops torchvision
+```
+
+Then, follow these steps:
+
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+from PIL import Image
+import requests
+import torch
+
+# load the processor
+processor = AutoProcessor.from_pretrained(
+    'allenai/Molmo-72B-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+# load the model
+model = AutoModelForCausalLM.from_pretrained(
+    'allenai/Molmo-72B-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+# process the image and text
+inputs = processor.process(
+    images=[Image.open(requests.get(""https://picsum.photos/id/237/536/354"", stream=True).raw)],
+    text=""Describe this image.""
+)
+
+# move inputs to the correct device and make a batch of size 1
+inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+
+# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
+output = model.generate_from_batch(
+    inputs,
+    GenerationConfig(max_new_tokens=200, stop_strings=""<|endoftext|>""),
+    tokenizer=processor.tokenizer
+)
+
+# only get generated tokens; decode them to text
+generated_tokens = output[0,inputs['input_ids'].size(1):]
+generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+# print the generated text
+print(generated_text)
+
+# >>> This image features an adorable black Labrador puppy sitting on a wooden deck. 
+#     The puppy is positioned in the center of the frame, looking up at the camera...
+```
+
+To make inference more efficient, run with autocast:
+
+
+```python
+with torch.autocast(device_type=""cuda"", enabled=True, dtype=torch.bfloat16):
+  output = model.generate_from_batch(
+      inputs,
+      GenerationConfig(max_new_tokens=200, stop_strings=""<|endoftext|>""),
+      tokenizer=processor.tokenizer
+  )
+```
+
+We did most of our evaluation in this setting (autocast on, but float32 weights)
+
+To even further reduce the memory requirements, the model can be run with bfloat16 weights:
+
+```
+model.to(dtype=torch.bfloat16)
+inputs[""images""] = inputs[""images""].to(torch.bfloat16)
+output = model.generate_from_batch(
+    inputs,
+    GenerationConfig(max_new_tokens=200, stop_strings=""<|endoftext|>""),
+    tokenizer=processor.tokenizer
+)
+```
+Note that we have observed that this can change the output of the model compared to running with float32 weights.
+
+## Evaluations 
+
+| Model                       | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating |
+|-----------------------------|-----------------------------------------|-----------------------------|
+| **Molmo 72B (this model)**  | **81.2**                                | **1077**                    |
+| Molmo 7B-D                  | 77.3                                    | 1056                        |
+| Molmo 7B-O                  | 74.6                                    | 1051                        |
+| MolmoE 1B                   | 68.6                                    | 1032                        |
+| GPT-4o                      | 78.5                                    | 1079                        |
+| GPT-4V                      | 71.1                                    | 1041                        |
+| Gemini 1.5 Pro              | 78.3                                    | 1074                        |
+| Gemini 1.5 Flash            | 75.1                                    | 1054                        |
+| Claude 3.5 Sonnet           | 76.7                                    | 1069                        |
+| Claude 3 Opus               | 66.4                                    |  971                        |
+| Claude 3 Haiku              | 65.3                                    |  999                        |
+| Qwen VL2 72B                | 79.4                                    | 1037                        |
+| Qwen VL2 7B                 | 73.7                                    | 1025                        |
+| Intern VL2 LLAMA 76B        | 77.1                                    | 1018                        |
+| Intern VL2 8B               | 69.4                                    |  953                        |
+| Pixtral 12B                 | 69.5                                    | 1016                        |
+| Phi3.5-Vision 4B            | 59.7                                    |  982                        |
+| PaliGemma 3B                | 50.0                                    |  937                        |
+| LLAVA OneVision 72B         | 76.6                                    | 1051                        |
+| LLAVA OneVision 7B          | 72.0                                    | 1024                        |
+| Cambrian-1 34B              | 66.8                                    |  953                        |
+| Cambrian-1 8B               | 63.4                                    |  952                        |
+| xGen - MM - Interleave 4B   | 59.5                                    |  979                        |
+| LLAVA-1.5 13B               | 43.9                                    |  960                        |
+| LLAVA-1.5 7B                | 40.7                                    |  951                        |
+
+*Benchmarks: AI2D test, ChartQA test, VQA v2.0 test, DocQA test, InfographicVQA test, TextVQA val, RealWorldQA, MMMU val, MathVista testmini, CountBenchQA, Flickr Count (we collected this new dataset that is significantly harder than CountBenchQA).*
+
+## FAQs
+
+### I'm getting an error a broadcast error when processing images!
+
+Your image might not be in RGB format. You can convert it using the following code snippet:
+
+```python
+from PIL import Image
+
+image = Image.open(...)
+
+if image.mode != ""RGB"":
+    image = image.convert(""RGB"")
+```
+
+### Molmo doesn't work great with transparent images!
+
+We received reports that Molmo models might struggle with transparent images. 
+For the time being, we recommend adding a white or dark background to your images before passing them to the model. The code snippet below shows how to do this using the Python Imaging Library (PIL):
+
+```python
+
+# Load the image
+url = ""...""
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Convert the image to grayscale to calculate brightness
+gray_image = image.convert('L')  # Convert to grayscale
+
+# Calculate the average brightness
+stat = ImageStat.Stat(gray_image)
+average_brightness = stat.mean[0]  # Get the average value
+
+# Define background color based on brightness (threshold can be adjusted)
+bg_color = (0, 0, 0) if average_brightness > 127 else (255, 255, 255)
+
+# Create a new image with the same size as the original, filled with the background color
+new_image = Image.new('RGB', image.size, bg_color)
+
+# Paste the original image on top of the background (use image as a mask if needed)
+new_image.paste(image, (0, 0), image if image.mode == 'RGBA' else None)
+
+# Now you can pass the new_image to Molmo
+processor = AutoProcessor.from_pretrained(
+    'allenai/Molmo-7B-D-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+```
+
+## License and Use
+
+This model is licensed under Apache 2.0. It is intended for research and educational use.
+For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
+The base model used is Qwen2-72B, whose license (the Tongyi Qianwen license) you can find [here](https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE).","{""id"": ""allenai/Molmo-72B-0924"", ""author"": ""allenai"", ""sha"": ""2ca845922396b7a5f7086bfda3fca6b8ecd1c8f3"", ""last_modified"": ""2024-10-10 23:19:15+00:00"", ""created_at"": ""2024-09-25 06:23:32+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1573, ""downloads_all_time"": null, ""likes"": 284, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""molmo"", ""text-generation"", ""multimodal"", ""olmo"", ""pixmo"", ""image-text-to-text"", ""conversational"", ""custom_code"", ""en"", ""arxiv:2409.17146"", ""base_model:Qwen/Qwen2-72B"", ""base_model:finetune:Qwen/Qwen2-72B"", ""license:apache-2.0"", ""autotrain_compatible"", ""region:us""], ""pipeline_tag"": ""image-text-to-text"", 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MythoMax-L2-13B-GPTQ_finetunes_20250426_215237.csv_finetunes_20250426_215237.csv

@@ -0,0 +1,315 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+TheBloke/MythoMax-L2-13B-GPTQ,"---
+language:
+- en
+license: other
+model_name: MythoMax L2 13B
+base_model: Gryphe/MythoMax-L2-13b
+inference: false
+model_creator: Gryphe
+model_type: llama
+prompt_template: '```
+
+  {system_message}
+
+
+  ### Instruction:
+
+  {prompt}
+
+  (For roleplay purposes, I suggest the following - Write <CHAR NAME>''s next reply
+  in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)
+
+
+  ### Response:
+
+
+  ```
+
+  '
+quantized_by: TheBloke
+---
+
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+<img src=""https://i.imgur.com/EBdldam.jpg"" alt=""TheBlokeAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+</div>
+<div style=""display: flex; justify-content: space-between; width: 100%;"">
+    <div style=""display: flex; flex-direction: column; align-items: flex-start;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://discord.gg/theblokeai"">Chat & support: TheBloke's Discord server</a></p>
+    </div>
+    <div style=""display: flex; flex-direction: column; align-items: flex-end;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://www.patreon.com/TheBlokeAI"">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<div style=""text-align:center; margin-top: 0em; margin-bottom: 0em""><p style=""margin-top: 0.25em; margin-bottom: 0em;"">TheBloke's LLM work is generously supported by a grant from <a href=""https://a16z.com"">andreessen horowitz (a16z)</a></p></div>
+<hr style=""margin-top: 1.0em; margin-bottom: 1.0em;"">
+<!-- header end -->
+
+# MythoMax L2 13B - GPTQ
+- Model creator: [Gryphe](https://huggingface.co/Gryphe)
+- Original model: [MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b)
+
+<!-- description start -->
+## Description
+
+This repo contains GPTQ model files for [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b).
+
+Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
+
+<!-- description end -->
+<!-- repositories-available start -->
+## Repositories available
+
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MythoMax-L2-13B-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF)
+* [Gryphe's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Gryphe/MythoMax-L2-13b)
+<!-- repositories-available end -->
+
+<!-- prompt-template start -->
+## Prompt template: Custom
+
+```
+{system_message}
+
+### Instruction:
+{prompt}
+(For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)
+
+### Response:
+
+```
+
+
+<!-- prompt-template end -->
+<!-- licensing start -->
+## Licensing
+
+The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
+
+As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
+
+In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b).
+<!-- licensing end -->
+<!-- README_GPTQ.md-provided-files start -->
+## Provided files and GPTQ parameters
+
+Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
+
+Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
+
+All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
+
+<details>
+  <summary>Explanation of GPTQ parameters</summary>
+
+- Bits: The bit size of the quantised model.
+- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. ""None"" is the lowest possible value.
+- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
+- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
+- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
+- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used.  Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
+- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
+
+</details>
+
+| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
+| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
+| [main](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | 
+| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
+| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | 
+| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
+| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | 
+| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
+
+<!-- README_GPTQ.md-provided-files end -->
+
+<!-- README_GPTQ.md-download-from-branches start -->
+## How to download from branches
+
+- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/MythoMax-L2-13B-GPTQ:main`
+- With Git, you can clone a branch with:
+```
+git clone --single-branch --branch main https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ
+```
+- In Python Transformers code, the branch is the `revision` parameter; see below.
+<!-- README_GPTQ.md-download-from-branches end -->
+<!-- README_GPTQ.md-text-generation-webui start -->
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/MythoMax-L2-13B-GPTQ`.
+  - To download from a specific branch, enter for example `TheBloke/MythoMax-L2-13B-GPTQ:main`
+  - see Provided Files above for the list of branches for each option.
+3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say ""Done"".
+5. In the top left, click the refresh icon next to **Model**.
+6. In the **Model** dropdown, choose the model you just downloaded: `MythoMax-L2-13B-GPTQ`
+7. The model will automatically load, and is now ready for use!
+8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
+  * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
+9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
+<!-- README_GPTQ.md-text-generation-webui end -->
+
+<!-- README_GPTQ.md-use-from-python start -->
+## How to use this GPTQ model from Python code
+
+### Install the necessary packages
+
+Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
+
+```shell
+pip3 install transformers>=4.32.0 optimum>=1.12.0
+pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7
+```
+
+If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
+
+```shell
+pip3 uninstall -y auto-gptq
+git clone https://github.com/PanQiWei/AutoGPTQ
+cd AutoGPTQ
+pip3 install .
+```
+
+### For CodeLlama models only: you must use Transformers 4.33.0 or later.
+
+If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
+```shell
+pip3 uninstall -y transformers
+pip3 install git+https://github.com/huggingface/transformers.git
+```
+
+### You can then use the following code
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_name_or_path = ""TheBloke/MythoMax-L2-13B-GPTQ""
+# To use a different branch, change revision
+# For example: revision=""main""
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map=""auto"",
+                                             trust_remote_code=False,
+                                             revision=""main"")
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+
+prompt = ""Tell me about AI""
+prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+
+'''
+
+print(""\n\n*** Generate:"")
+
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+
+# Inference can also be done using transformers' pipeline
+
+print(""*** Pipeline:"")
+pipe = pipeline(
+    ""text-generation"",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.1
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
+```
+<!-- README_GPTQ.md-use-from-python end -->
+
+<!-- README_GPTQ.md-compatibility start -->
+## Compatibility
+
+The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
+
+[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
+
+[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
+<!-- README_GPTQ.md-compatibility end -->
+
+<!-- footer start -->
+<!-- 200823 -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Aemon Algiz.
+
+**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
+
+
+Thank you to all my generous patrons and donaters!
+
+And thank you again to a16z for their generous grant.
+
+<!-- footer end -->
+
+# Original model card: Gryphe's MythoMax L2 13B
+
+An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure.
+
+The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML).
+
+This model is proficient at both roleplaying and storywriting due to its unique nature.
+
+Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) (You're the best!)
+
+## Model details
+
+The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
+
+This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
+
+## Prompt Format
+
+This model primarily uses Alpaca formatting, so for optimal model performance, use:
+```
+<System prompt/Character Card>
+
+### Instruction:
+Your instruction or question here.
+For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
+
+### Response:
+```
+ 
+---
+license: other
+---
+","{""id"": ""TheBloke/MythoMax-L2-13B-GPTQ"", ""author"": ""TheBloke"", ""sha"": ""2a6a8d47826271a065b7589f001c359022b4950d"", ""last_modified"": ""2023-09-27 12:45:40+00:00"", ""created_at"": ""2023-08-11 07:27:24+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 5227, ""downloads_all_time"": null, ""likes"": 204, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""en"", ""base_model:Gryphe/MythoMax-L2-13b"", ""base_model:quantized:Gryphe/MythoMax-L2-13b"", ""license:other"", ""autotrain_compatible"", ""text-generation-inference"", ""4-bit"", ""gptq"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: Gryphe/MythoMax-L2-13b\nlanguage:\n- en\nlicense: other\nmodel_name: MythoMax L2 13B\ninference: false\nmodel_creator: Gryphe\nmodel_type: llama\nprompt_template: '```\n\n  {system_message}\n\n\n  ### Instruction:\n\n  {prompt}\n\n  (For roleplay purposes, I suggest the following - Write <CHAR NAME>''s next reply\n  in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)\n\n\n  ### Response:\n\n\n  ```\n\n  '\nquantized_by: TheBloke"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""quantization_config"": {""bits"": 4, ""quant_method"": ""gptq""}, ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<s>"", ""lstrip"": false, ""normalized"": false, ""rstrip"": false, ""single_word"": false}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""</s>"", ""lstrip"": false, ""normalized"": false, ""rstrip"": false, ""single_word"": false}, ""pad_token"": null, ""unk_token"": {""__type"": ""AddedToken"", ""content"": ""<unk>"", ""lstrip"": false, ""normalized"": false, ""rstrip"": false, ""single_word"": false}}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='Notice', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='USE_POLICY.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='quantize_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""KBaba7/Quant"", ""bhaskartripathi/LLM_Quantization"", ""totolook/Quant"", ""FallnAI/Quantize-HF-Models"", ""ruslanmv/convert_to_gguf"", ""ming512/llm"", ""DGameHF/MSH-AI"", ""K00B404/LLM_Quantization""], ""safetensors"": {""parameters"": {""I32"": 1600143360, ""F16"": 429347840}, ""total"": 2029491200}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-09-27 12:45:40+00:00"", ""cardData"": ""base_model: Gryphe/MythoMax-L2-13b\nlanguage:\n- en\nlicense: other\nmodel_name: MythoMax L2 13B\ninference: false\nmodel_creator: Gryphe\nmodel_type: llama\nprompt_template: '```\n\n  {system_message}\n\n\n  ### Instruction:\n\n  {prompt}\n\n  (For roleplay purposes, I suggest the following - Write <CHAR NAME>''s next reply\n  in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.)\n\n\n  ### Response:\n\n\n  ```\n\n  '\nquantized_by: TheBloke"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""64d5e2dcabf475a808a24d42"", ""modelId"": ""TheBloke/MythoMax-L2-13B-GPTQ"", ""usedStorage"": 57044260131}",0,,0,"https://huggingface.co/paulrouge/test-finetune, https://huggingface.co/paulrouge/test-lora-3",2,,0,,0,"DGameHF/MSH-AI, FallnAI/Quantize-HF-Models, K00B404/LLM_Quantization, KBaba7/Quant, bhaskartripathi/LLM_Quantization, huggingface/InferenceSupport/discussions/new?title=TheBloke/MythoMax-L2-13B-GPTQ&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BTheBloke%2FMythoMax-L2-13B-GPTQ%5D(%2FTheBloke%2FMythoMax-L2-13B-GPTQ)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, ming512/llm, ruslanmv/convert_to_gguf, totolook/Quant",9

Nous-Capybara-34B_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,125 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+NousResearch/Nous-Capybara-34B,"---
+language:
+- eng
+tags:
+- sft
+- Yi-34B-200K
+license:
+- mit
+datasets:
+- LDJnr/Capybara
+- LDJnr/LessWrong-Amplify-Instruct
+- LDJnr/Pure-Dove
+- LDJnr/Verified-Camel
+---
+
+## **Nous-Capybara-34B V1.9**
+
+**This is trained on the Yi-34B model with 200K context length, for 3 epochs on the Capybara dataset!**
+
+**First 34B Nous model and first 200K context length Nous model!**
+
+The Capybara series is the first Nous collection of models made by fine-tuning mostly on data created by Nous in-house. 
+
+We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly regarded datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin).
+
+While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing current models, this is signficant when it comes to scaling implications for our next generation of models once we scale our novel syntheiss methods to significantly more examples.
+
+## Process of creation and special thank yous!
+
+This model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D.(LDJ) (Paper coming soon), as well as significant dataset formation contributions by J-Supha and general compute and experimentation management by Jeffrey Q. during ablations.
+
+Special thank you to **A16Z** for sponsoring our training, as well as **Yield Protocol** for their support in financially sponsoring resources during the R&D of this project.
+
+## Thank you to those of you that have indirectly contributed!
+
+While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis.
+
+The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project.
+
+Datasets in Blue are in-house curations that previously existed prior to Capybara.
+
+![Capybara](https://i.imgur.com/yB58OoD.jpeg)
+
+
+## Prompt Format
+
+The reccomended model usage is:
+
+
+Prefix: ``USER:``
+
+Suffix: ``ASSISTANT:``
+
+Stop token: ``</s>``
+
+
+## Mutli-Modality!
+
+ - We currently have a Multi-modal model based on Capybara V1.9! 
+https://huggingface.co/NousResearch/Obsidian-3B-V0.5
+it is currently only available as a 3B sized model but larger versions coming!
+
+
+## Notable Features:
+
+ - Uses Yi-34B model as the base which is trained for 200K context length!
+
+ - Over 60% of the dataset is comprised of multi-turn conversations.(Most models are still only trained for single-turn conversations and no back and forths!)
+
+ - Over 1,000 tokens average per conversation example! (Most models are trained on conversation data that is less than 300 tokens per example.)
+
+ - Able to effectively do complex summaries of advanced topics and studies. (trained on hundreds of advanced difficult summary tasks developed in-house)
+
+ - Ability to recall information upto late 2022 without internet.
+
+ - Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth details and philosophies about the nature of reality, reasoning, rationality, self-improvement and related concepts.
+
+## Example Outputs from Capybara V1.9 7B version! (examples from 34B coming soon):
+
+![Capybara](https://img001.prntscr.com/file/img001/T9yYxR1xQSaK_UGdy3t2Cw.png)
+
+![Capybara](https://img001.prntscr.com/file/img001/DQXqmKbsQQOIcgny1eoGNA.png)
+
+![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png)
+
+## Benchmarks! (Coming soon!)
+ 
+
+## Future model sizes
+
+Capybara V1.9 now currently has a 3B, 7B and 34B size, and we plan to eventually have a 13B and 70B version in the future, as well as a potential 1B version based on phi-1.5 or Tiny Llama.
+
+## How you can help!
+
+In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations. 
+
+If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
+
+## Dataset contamination.
+
+We have checked the capybara dataset for contamination for several of the most popular datasets and can confirm that there is no contaminaton found.
+
+We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level.
+
+The following are benchmarks we checked for contamination against our dataset:
+
+- HumanEval
+
+- AGIEval
+
+- TruthfulQA
+
+- MMLU
+
+- GPT4All
+
+```
+@article{daniele2023amplify-instruct,
+  title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
+  author={Daniele, Luigi and Suphavadeeprasit},
+  journal={arXiv preprint arXiv:(comming soon)},
+  year={2023}
+}
+```","{""id"": ""NousResearch/Nous-Capybara-34B"", ""author"": ""NousResearch"", ""sha"": ""6beb706364038ba43350d85734975377236e9546"", ""last_modified"": ""2023-12-28 12:56:35+00:00"", ""created_at"": ""2023-11-13 05:01:41+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 566, ""downloads_all_time"": null, ""likes"": 259, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""llama"", ""text-generation"", ""sft"", ""Yi-34B-200K"", ""eng"", ""dataset:LDJnr/Capybara"", ""dataset:LDJnr/LessWrong-Amplify-Instruct"", ""dataset:LDJnr/Pure-Dove"", ""dataset:LDJnr/Verified-Camel"", ""license:mit"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""datasets:\n- LDJnr/Capybara\n- LDJnr/LessWrong-Amplify-Instruct\n- LDJnr/Pure-Dove\n- LDJnr/Verified-Camel\nlanguage:\n- eng\nlicense:\n- mit\ntags:\n- sft\n- Yi-34B-200K"", ""widget_data"": null, ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<|startoftext|>"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00003-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00004-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00005-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00006-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00007-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenization_yi.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""eduagarcia/open_pt_llm_leaderboard"", ""Twent/NousResearch-Nous-Capybara-34B"", ""DomArruda/NousResearch-Nous-Capybara-34B"", ""zksneil/NousResearch-Nous-Capybara-34B"", ""roppwer/NousResearch-Nous-Capybara-34B"", ""Adeohluwa/NousResearch-Nous-Capybara-34B"", ""iblfe/test""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-12-28 12:56:35+00:00"", ""cardData"": ""datasets:\n- LDJnr/Capybara\n- LDJnr/LessWrong-Amplify-Instruct\n- LDJnr/Pure-Dove\n- LDJnr/Verified-Camel\nlanguage:\n- eng\nlicense:\n- mit\ntags:\n- sft\n- Yi-34B-200K"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""6551adb57490049d625a19fc"", ""modelId"": ""NousResearch/Nous-Capybara-34B"", ""usedStorage"": 137556951568}",0,,0,,0,"https://huggingface.co/TheBloke/Nous-Capybara-34B-GGUF, https://huggingface.co/mradermacher/Nous-Capybara-34B-GGUF, https://huggingface.co/TheBloke/Nous-Capybara-34B-AWQ, https://huggingface.co/TheBloke/Nous-Capybara-34B-GPTQ, https://huggingface.co/mradermacher/Nous-Capybara-34B-i1-GGUF",5,,0,"Adeohluwa/NousResearch-Nous-Capybara-34B, DomArruda/NousResearch-Nous-Capybara-34B, Twent/NousResearch-Nous-Capybara-34B, eduagarcia/open_pt_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=NousResearch/Nous-Capybara-34B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BNousResearch%2FNous-Capybara-34B%5D(%2FNousResearch%2FNous-Capybara-34B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, iblfe/test, roppwer/NousResearch-Nous-Capybara-34B, zksneil/NousResearch-Nous-Capybara-34B",8

Nous-Hermes-2-Vision-Alpha_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,125 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+NousResearch/Nous-Hermes-2-Vision-Alpha,"---
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- mistral
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+- multimodal
+- llava
+model-index:
+- name: Nous-Hermes-2-Vision
+  results: []
+license: apache-2.0
+language:
+- en
+---
+
+# Nous-Hermes-2-Vision - Mistral 7B
+
+
+![image/png](https://camo.githubusercontent.com/b09dc35a93b4b70748fa4e2f307b011cd3d548369dd926ec9a2d3a51f7b3721e/68747470733a2f2f66696c65732e6f616975736572636f6e74656e742e636f6d2f66696c652d6b4437565358734f5649576472624b3042353662686644363f73653d323032332d31322d3033543137253341333425334135385a2673703d722673763d323032312d30382d30362673723d6226727363633d6d61782d6167652533443331353336303030253243253230696d6d757461626c6526727363643d6174746163686d656e7425334225323066696c656e616d6525334439643530333039622d356236342d343964302d623832362d6165316638366132396661382e77656270267369673d50396973694b4679654a54435a47424b526d45494b3043586e6e55676c6334704a583071312532425478666a34253344)
+
+*In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM ""Hermes,"" a system crafted to navigate the complex intricacies of human discourse with celestial finesse.*
+
+## Model description
+
+Nous-Hermes-2-Vision stands as a pioneering Vision-Language Model, leveraging advancements from the renowned **OpenHermes-2.5-Mistral-7B** by teknium. This model incorporates two pivotal enhancements, setting it apart as a cutting-edge solution:
+
+- **SigLIP-400M Integration**: Diverging from traditional approaches that rely on substantial 3B vision encoders, Nous-Hermes-2-Vision harnesses the formidable SigLIP-400M. This strategic choice not only streamlines the model's architecture, making it more lightweight, but also capitalizes on SigLIP's remarkable capabilities. The result? A remarkable boost in performance that defies conventional expectations.
+
+- **Custom Dataset Enriched with Function Calling**: Our model's training data includes a unique feature – function calling. This distinctive addition transforms Nous-Hermes-2-Vision into a **Vision-Language Action Model**. Developers now have a versatile tool at their disposal, primed for crafting a myriad of ingenious automations.
+
+This project is led by [qnguyen3](https://twitter.com/stablequan) and [teknium](https://twitter.com/Teknium1).
+## Training
+### Dataset
+- 220K from **LVIS-INSTRUCT4V**
+- 60K from **ShareGPT4V**
+- 150K Private **Function Calling Data**
+- 50K conversations from teknium's **OpenHermes-2.5**
+
+## Usage
+### Prompt Format
+- Like other LLaVA's variants, this model uses Vicuna-V1 as its prompt template. Please refer to `conv_llava_v1` in [this file](https://github.com/qnguyen3/hermes-llava/blob/main/llava/conversation.py)
+- For Gradio UI, please visit this [GitHub Repo](https://github.com/qnguyen3/hermes-llava)
+
+### Function Calling
+- For functiong calling, the message should start with a `<fn_call>` tag. Here is an example:
+
+```json
+<fn_call>{
+  ""type"": ""object"",
+  ""properties"": {
+    ""bus_colors"": {
+      ""type"": ""array"",
+      ""description"": ""The colors of the bus in the image."",
+      ""items"": {
+        ""type"": ""string"",
+        ""enum"": [""red"", ""blue"", ""green"", ""white""]
+      }
+    },
+    ""bus_features"": {
+      ""type"": ""string"",
+      ""description"": ""The features seen on the back of the bus.""
+    },
+    ""bus_location"": {
+      ""type"": ""string"",
+      ""description"": ""The location of the bus (driving or pulled off to the side)."",
+      ""enum"": [""driving"", ""pulled off to the side""]
+    }
+  }
+}
+```
+
+Output:
+```json
+{
+  ""bus_colors"": [""red"", ""white""],
+  ""bus_features"": ""An advertisement"",
+  ""bus_location"": ""driving""
+}
+```
+
+## Example
+
+### Chat
+![image/png](https://i.ibb.co/tMg8h2t/Screenshot-from-2023-12-04-00-13-59.png)
+
+### Function Calling
+Input image:
+
+![image/png](https://www.slcmenu.com/wp-content/uploads/2017/11/In-N-Out-Burger-menu-2020-982x1024.jpg)
+
+Input message:
+```json
+<fn_call>{
+    ""type"": ""object"",
+    ""properties"": {
+      ""food_list"": {
+        ""type"": ""array"",
+        ""description"": ""List of all the food"",
+        ""items"": {
+          ""type"": ""string"",
+        }
+      },
+    }
+}
+```
+
+Output:
+```json
+{
+    ""food_list"": [
+        ""Double Burger"",
+        ""Cheeseburger"",
+        ""French Fries"",
+        ""Shakes"",
+        ""Coffee""
+    ]
+}
+```
+","{""id"": ""NousResearch/Nous-Hermes-2-Vision-Alpha"", ""author"": ""NousResearch"", ""sha"": ""cb1e43865b0a23d3eb0bafcb0828e9a4db379ac8"", ""last_modified"": ""2023-12-03 17:25:41+00:00"", ""created_at"": ""2023-11-28 23:18:55+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 19, ""downloads_all_time"": null, ""likes"": 301, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""llava_mistral"", ""text-generation"", ""mistral"", ""instruct"", ""finetune"", ""chatml"", ""gpt4"", ""synthetic data"", ""distillation"", ""multimodal"", ""llava"", ""conversational"", ""en"", ""base_model:mistralai/Mistral-7B-v0.1"", ""base_model:finetune:mistralai/Mistral-7B-v0.1"", ""license:apache-2.0"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: mistralai/Mistral-7B-v0.1\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- mistral\n- instruct\n- finetune\n- chatml\n- gpt4\n- synthetic data\n- distillation\n- multimodal\n- llava\nmodel-index:\n- name: Nous-Hermes-2-Vision\n  results: []"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": [{""name"": ""Nous-Hermes-2-Vision"", ""results"": []}], ""config"": {""architectures"": [""LlavaMistralForCausalLM""], ""model_type"": ""llava_mistral"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""chat_template"": ""{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": true}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='mm_projector.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00002.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00002.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='trainer_state.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [""ContentStreamSteve/NousResearch-Nous-Hermes-2-Vision"", ""michaelfarina/NousResearch-Nous-Hermes-2-Vision-Alpha"", ""renamalvs/NousResearch-Nous-Hermes-2-Vision-Alpha"", ""IshanExtreme/NousResearch-Nous-Hermes-2-Vision-Alpha""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-12-03 17:25:41+00:00"", ""cardData"": ""base_model: mistralai/Mistral-7B-v0.1\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- mistral\n- instruct\n- finetune\n- chatml\n- gpt4\n- synthetic data\n- distillation\n- multimodal\n- llava\nmodel-index:\n- name: Nous-Hermes-2-Vision\n  results: []"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""_id"": ""6566755f5ed6ca5f18e0cf79"", ""modelId"": ""NousResearch/Nous-Hermes-2-Vision-Alpha"", ""usedStorage"": 15396264241}",0,,0,,0,,0,,0,"ContentStreamSteve/NousResearch-Nous-Hermes-2-Vision, IshanExtreme/NousResearch-Nous-Hermes-2-Vision-Alpha, huggingface/InferenceSupport/discussions/new?title=NousResearch/Nous-Hermes-2-Vision-Alpha&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BNousResearch%2FNous-Hermes-2-Vision-Alpha%5D(%2FNousResearch%2FNous-Hermes-2-Vision-Alpha)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, michaelfarina/NousResearch-Nous-Hermes-2-Vision-Alpha, renamalvs/NousResearch-Nous-Hermes-2-Vision-Alpha",5

Nous-Hermes-2-Yi-34B_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,353 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+NousResearch/Nous-Hermes-2-Yi-34B,"---
+base_model: 01-ai/Yi-34B
+tags:
+- yi
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+model-index:
+- name: Nous-Hermes-2-Yi-34B
+  results: []
+license: apache-2.0
+language:
+- en
+datasets:
+- teknium/OpenHermes-2.5
+---
+
+# Nous Hermes 2 - Yi-34B
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oOqrUeAQejuQOra7fNlzG.png)
+
+## Model description
+
+Nous Hermes 2 - Yi-34B is a state of the art Yi Fine-tune.
+
+Nous Hermes 2 Yi 34B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape.
+
+# Table of Contents
+1. [Example Outputs](#example-outputs)
+    - Discussing the Laws of Gravity
+    - Create a Flask based FTP Server
+2. [Benchmark Results](#benchmark-results)
+    - GPT4All
+    - AGIEval
+    - BigBench
+    - Averages Compared
+3. [Prompt Format](#prompt-format)
+4. [Quantized Models](#quantized-models)
+
+
+## Example Outputs
+
+### Discussions about the Law of Gravity:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/J6Rmdj1VOVN7ry_uGL1PK.png)
+
+### Create an FTP Server in FLASK:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/B5eu8OvQlg8rINBJGxbB7.png)
+
+## Benchmark Results
+
+Nous-Hermes 2 on Yi 34B outperforms all Nous-Hermes & Open-Hermes models of the past, achieving new heights in all benchmarks for a Nous Research LLM as well as surpassing many popular finetunes. 
+
+# Benchmarks Compared
+
+### GPT4All:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/91onORUcUrAqTb3b9mG5e.png)
+
+### AGIEval:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/hqDpMlKpINfDf4PmB31uW.png)
+
+### BigBench:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/uh8mZZg_wZinFysxcfLSF.png)
+
+### TruthfulQA:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/N_cX6YAWjJsvClotuoPdH.png)
+
+
+
+## GPT4All
+GPT-4All Benchmark Set
+```
+|    Task     |Version| Metric |Value |   |Stderr|
+|-------------|------:|--------|-----:|---|-----:|
+|arc_challenge|      0|acc     |0.6067|_  |0.0143|
+|             |       |acc_norm|0.6416|_  |0.0140|
+|arc_easy     |      0|acc     |0.8594|_  |0.0071|
+|             |       |acc_norm|0.8569|_  |0.0072|
+|boolq        |      1|acc     |0.8859|_  |0.0056|
+|hellaswag    |      0|acc     |0.6407|_  |0.0048|
+|             |       |acc_norm|0.8388|_  |0.0037|
+|openbookqa   |      0|acc     |0.3520|_  |0.0214|
+|             |       |acc_norm|0.4760|_  |0.0224|
+|piqa         |      0|acc     |0.8215|_  |0.0089|
+|             |       |acc_norm|0.8303|_  |0.0088|
+|winogrande   |      0|acc     |0.7908|_  |0.0114|
+Average: 76.00%
+```  
+
+AGI-Eval
+```
+|             Task             |Version| Metric |Value |   |Stderr|
+|------------------------------|------:|--------|-----:|---|-----:|
+|agieval_aqua_rat              |      0|acc     |0.3189|_  |0.0293|
+|                              |       |acc_norm|0.2953|_  |0.0287|
+|agieval_logiqa_en             |      0|acc     |0.5438|_  |0.0195|
+|                              |       |acc_norm|0.4977|_  |0.0196|
+|agieval_lsat_ar               |      0|acc     |0.2696|_  |0.0293|
+|                              |       |acc_norm|0.2087|_  |0.0269|
+|agieval_lsat_lr               |      0|acc     |0.7078|_  |0.0202|
+|                              |       |acc_norm|0.6255|_  |0.0215|
+|agieval_lsat_rc               |      0|acc     |0.7807|_  |0.0253|
+|                              |       |acc_norm|0.7063|_  |0.0278|
+|agieval_sat_en                |      0|acc     |0.8689|_  |0.0236|
+|                              |       |acc_norm|0.8447|_  |0.0253|
+|agieval_sat_en_without_passage|      0|acc     |0.5194|_  |0.0349|
+|                              |       |acc_norm|0.4612|_  |0.0348|
+|agieval_sat_math              |      0|acc     |0.4409|_  |0.0336|
+|                              |       |acc_norm|0.3818|_  |0.0328|
+Average: 50.27%
+```  
+
+BigBench Reasoning Test
+```
+|                      Task                      |Version|       Metric        |Value |   |Stderr|
+|------------------------------------------------|------:|---------------------|-----:|---|-----:|
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5737|_  |0.0360|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7263|_  |0.0232|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3953|_  |0.0305|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.4457|_  |0.0263|
+|                                                |       |exact_str_match      |0.0000|_  |0.0000|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2820|_  |0.0201|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2186|_  |0.0156|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4733|_  |0.0289|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.5200|_  |0.0224|
+|bigbench_navigate                               |      0|multiple_choice_grade|0.4910|_  |0.0158|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.7495|_  |0.0097|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|0.5938|_  |0.0232|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.3808|_  |0.0154|
+|bigbench_snarks                                 |      0|multiple_choice_grade|0.8066|_  |0.0294|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5101|_  |0.0159|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3850|_  |0.0154|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2160|_  |0.0116|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1634|_  |0.0088|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4733|_  |0.0289|
+Average: 46.69%
+```  
+
+TruthfulQA:
+```
+|    Task     |Version|Metric|Value |   |Stderr|
+|-------------|------:|------|-----:|---|-----:|
+|truthfulqa_mc|      1|mc1   |0.4333|_  |0.0173|
+|             |       |mc2   |0.6034|_  |0.0149|
+```
+
+Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
+```
+|     Bench     | OpenHermes-2.5 Mistral 7B | Nous-Hermes-2-Yi-34B | Change/OpenHermes2 |
+|---------------|---------------------------|----------------------|--------------------|
+|GPT4All        |                      73.12|                 76.00|               +2.88|
+|---------------------------------------------------------------------------------------|
+|BigBench       |                      40.96|                 46.69|               +5.73|
+|---------------------------------------------------------------------------------------|
+|AGI Eval       |                      43.07|                 50.27|               +7.20|
+|---------------------------------------------------------------------------------------|
+|TruthfulQA     |                      53.04|                 60.34|               +7.30|
+|---------------------------------------------------------------------------------------|
+|Total Score    |                     210.19|                233.30|              +23.11|
+|---------------------------------------------------------------------------------------|
+|Average Total  |                      52.38|                 58.33|               +5.95|
+```
+
+# Prompt Format
+
+Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
+
+System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
+
+This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
+
+This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
+
+Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
+```
+<|im_start|>system
+You are ""Hermes 2"", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
+<|im_start|>user
+Hello, who are you?<|im_end|>
+<|im_start|>assistant
+Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
+```
+
+This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
+`tokenizer.apply_chat_template()` method:
+
+```python
+messages = [
+    {""role"": ""system"", ""content"": ""You are Hermes 2.""},
+    {""role"": ""user"", ""content"": ""Hello, who are you?""}
+]
+gen_input = tokenizer.apply_chat_template(message, return_tensors=""pt"")
+model.generate(**gen_input)
+```
+
+When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
+that the model continues with an assistant response.
+
+To utilize the prompt format without a system prompt, simply leave the line out.
+
+When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
+In LM-Studio, simply select the ChatML Prefix on the settings side pane:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
+
+# Quantized Models:
+
+GGUF: https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B-GGUF
+
+[<img src=""https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png"" alt=""Built with Axolotl"" width=""200"" height=""32""/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+","{""id"": ""NousResearch/Nous-Hermes-2-Yi-34B"", ""author"": ""NousResearch"", ""sha"": ""fcb0a8847e76aea14aba9aa44009d4418ad7c18f"", ""last_modified"": ""2024-02-20 09:17:20+00:00"", ""created_at"": ""2023-12-23 19:47:48+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 179, ""downloads_all_time"": null, ""likes"": 249, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""yi"", ""instruct"", ""finetune"", ""chatml"", ""gpt4"", ""synthetic data"", ""distillation"", ""conversational"", ""en"", ""dataset:teknium/OpenHermes-2.5"", ""base_model:01-ai/Yi-34B"", ""base_model:finetune:01-ai/Yi-34B"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: 01-ai/Yi-34B\ndatasets:\n- teknium/OpenHermes-2.5\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- yi\n- instruct\n- finetune\n- chatml\n- gpt4\n- synthetic data\n- distillation\nmodel-index:\n- name: Nous-Hermes-2-Yi-34B\n  results: []"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": [{""name"": ""Nous-Hermes-2-Yi-34B"", ""results"": []}], ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<|startoftext|>"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<unk>"", ""chat_template"": ""{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": false}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00007-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00008-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00009-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00010-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00011-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00012-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00013-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00014-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00015-of-00015.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""eduagarcia/open_pt_llm_leaderboard"", ""KBaba7/Quant"", ""prometheus-eval/BiGGen-Bench-Leaderboard"", ""bhaskartripathi/LLM_Quantization"", ""JohnPorkEater/TherapyGPT"", ""totolook/Quant"", ""FallnAI/Quantize-HF-Models"", ""bardsai/performance-llm-board"", ""ogegadavis254/Raxder"", ""Taranosaurus/Tokenizaminer"", ""ogegadavis254/Raxder_AI"", ""ruslanmv/convert_to_gguf"", ""prashantstha17/NousResearch-Nous-Hermes-2-Yi-34B"", ""trueequals/NousResearch-Nous-Hermes-2-Yi-34B"", ""Ondra18cz/NousResearch-Nous-Hermes-2-Yi-34B"", ""Ondra18cz/NousResearch-Nous-Hermes-2-Yi-34B-1.01"", ""JerroldLee/NousResearch-Nous-Hermes-2-Yi-34B"", ""ogegadavis254/Raxder_Coding_Model"", ""tangjianping/NousResearch-Nous-Hermes-2-Yi-34B"", ""ogegadavis254/Bible_Learner_AI"", ""joaopaulopresa/workshop_llm_ufg_chatbot"", ""ogegadavis254/Raxder_Official-Model"", ""ogegadavis254/Coder"", ""ogegadavis254/api"", ""raxder-ai/Raxder"", ""raxder-ai/main"", ""raxder-ai/teresa"", ""ogegadavis254/2311"", ""raxder-ai/model0"", ""ogegadavis254/trial"", ""ogegadavis254/pdfchat"", ""raxder-ai/terry"", ""Xhaheen/AI_safety_testing"", ""Xhaheen/phoeniks_redteamers"", ""K00B404/LLM_Quantization""], ""safetensors"": {""parameters"": {""BF16"": 34388917248}, ""total"": 34388917248}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-02-20 09:17:20+00:00"", ""cardData"": ""base_model: 01-ai/Yi-34B\ndatasets:\n- teknium/OpenHermes-2.5\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- yi\n- instruct\n- finetune\n- chatml\n- gpt4\n- synthetic data\n- distillation\nmodel-index:\n- name: Nous-Hermes-2-Yi-34B\n  results: []"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""658739644bb41498f76f20ce"", ""modelId"": ""NousResearch/Nous-Hermes-2-Yi-34B"", ""usedStorage"": 68778931137}",0,"https://huggingface.co/gbstox/agronomYi-hermes-34B, https://huggingface.co/RedHatAI/Nous-Hermes-2-Yi-34B-pruned2.4, https://huggingface.co/RedHatAI/Nous-Hermes-2-Yi-34B-pruned50",3,,0,"https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF, https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-AWQ, https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GPTQ, https://huggingface.co/gbstox/agronomYi-hermes-34B-GGUF, https://huggingface.co/RedHatAI/Nous-Hermes-2-Yi-34B-marlin, https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-i1-GGUF, https://huggingface.co/qwp4w3hyb/Nous-Hermes-2-Yi-34B-iMat-GGUF, https://huggingface.co/tensorblock/Nous-Hermes-2-Yi-34B-GGUF, https://huggingface.co/itlwas/Nous-Hermes-2-Yi-34B-Q4_K_M-GGUF",9,"https://huggingface.co/OpenGVLab/InternVL2-40B, https://huggingface.co/dfurman/HermesBagel-34B-v0.1, https://huggingface.co/gotchu/merge-34b-1, https://huggingface.co/gotchu/merge-34b-2, https://huggingface.co/Bytes512/Queen, https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2, https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus, https://huggingface.co/wassemgtk/merge-Nous-Hermes-2-Yi-34B-Llama-3-8B-Instruct-12B, https://huggingface.co/wassemgtk/merge-Meta-Llama-3-8B-Instruct-Nous-Hermes-2-Yi-34B, https://huggingface.co/radna/XLA-InternVL2-40B",10,"FallnAI/Quantize-HF-Models, JohnPorkEater/TherapyGPT, K00B404/LLM_Quantization, KBaba7/Quant, Taranosaurus/Tokenizaminer, bardsai/performance-llm-board, bhaskartripathi/LLM_Quantization, eduagarcia/open_pt_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=NousResearch/Nous-Hermes-2-Yi-34B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BNousResearch%2FNous-Hermes-2-Yi-34B%5D(%2FNousResearch%2FNous-Hermes-2-Yi-34B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, ogegadavis254/Raxder, ogegadavis254/Raxder_AI, prometheus-eval/BiGGen-Bench-Leaderboard, totolook/Quant",13
+gbstox/agronomYi-hermes-34B,"---
+base_model: NousResearch/Nous-Hermes-2-Yi-34B
+datasets:
+- gbstox/agronomy-resources
+tags:
+- Yi-34B
+- instruct
+- finetune
+- agriculture
+language:
+- en
+---
+
+# AgronomYi-hermes-34B
+
+<img src=""https://cdn-uploads.huggingface.co/production/uploads/63042a3d7373aacccd896484/TwXNxFw8zSLuWjiYL41Bj.jpeg"" width=""500"" >
+
+# About
+AgronomYi is a fine tune of [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B), which uses Yi-34B as the base model.
+I fine tuned this with agronomy data (exclusively textbooks & university extension guides), full training data set [here](https://huggingface.co/datasets/gbstox/agronomy-resources)).
+AgronomYi outperforms all models on the benchmark except for gpt-4, and consistently beats the base model by 7-9% and the hermes fine tune by 3-5%. I take this to mean that even better results can be acheived with additional fine tuning, and larger models tend to perform better in general. 
+
+# Benchmark comparison
+[benchmark info here](https://github.com/gbstox/agronomy_llm_benchmarking)
+
+| Model Name | Score | Date Tested |
+|------------|-------|-------------|
+| gpt-4 | 85.71% | 2024-01-15 |
+| agronomYi-hermes-34b | 79.05% | 2024-01-15 |
+| mistral-medium | 77.14% | 2024-01-15 |
+| nous-hermes-yi-34b | 76.19% | 2024-01-15 |
+| mixtral-8x7b-instruct | 72.38% | 2024-01-15 |
+| claude-2 | 72.38% | 2024-01-15 |
+| yi-34b-chat | 71.43% | 2024-01-15 |
+| norm | 69.52% | 2024-01-17 |
+| openhermes-2.5-mistral-7b | 69.52% | 2024-01-15 |
+| gpt-3.5-turbo | 67.62% | 2024-01-15 |
+| mistral-7b-instruct | 61.9% | 2024-01-15 |
+","{""id"": ""gbstox/agronomYi-hermes-34B"", ""author"": ""gbstox"", ""sha"": ""6d7cb9cf177130c5a62e794a3e8d31d10e442545"", ""last_modified"": ""2024-01-18 03:19:53+00:00"", ""created_at"": ""2024-01-10 12:41:46+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 13, ""downloads_all_time"": null, ""likes"": 2, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""llama"", ""text-generation"", ""Yi-34B"", ""instruct"", ""finetune"", ""agriculture"", ""conversational"", ""en"", ""dataset:gbstox/agronomy-resources"", ""base_model:NousResearch/Nous-Hermes-2-Yi-34B"", ""base_model:finetune:NousResearch/Nous-Hermes-2-Yi-34B"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: 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""RepoSibling(rfilename='pytorch_model-00007-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00008-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00009-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00010-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00011-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00012-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00013-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00014-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00015-of-00015.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-01-18 03:19:53+00:00"", ""cardData"": ""base_model: NousResearch/Nous-Hermes-2-Yi-34B\ndatasets:\n- gbstox/agronomy-resources\nlanguage:\n- en\ntags:\n- Yi-34B\n- instruct\n- finetune\n- agriculture"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""659e908ab81bfb45fb0fd638"", ""modelId"": ""gbstox/agronomYi-hermes-34B"", ""usedStorage"": 137557081577}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=gbstox/agronomYi-hermes-34B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bgbstox%2FagronomYi-hermes-34B%5D(%2Fgbstox%2FagronomYi-hermes-34B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+https://huggingface.co/RedHatAI/Nous-Hermes-2-Yi-34B-pruned2.4,N/A,N/A,1,,0,,0,,0,,0,,0
+RedHatAI/Nous-Hermes-2-Yi-34B-pruned50,"---
+base_model: NousResearch/Nous-Hermes-2-Yi-34B
+inference: true
+model_type: llama
+quantized_by: mgoin
+tags:
+- nm-vllm
+- sparse
+---
+
+## Nous-Hermes-2-Yi-34B-pruned50
+This repo contains model files for [Nous Hermes 2 - Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) optimized for [NM-vLLM](https://github.com/neuralmagic/nm-vllm), a high-throughput serving engine for compressed LLMs.
+
+This model was pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
+
+## Inference
+Install [NM-vLLM](https://github.com/neuralmagic/nm-vllm) for fast inference and low memory-usage: 
+```bash
+pip install nm-vllm[sparse]
+```
+Run in a Python pipeline for local inference:
+```python
+from vllm import LLM, SamplingParams
+
+model = LLM(""nm-testing/Nous-Hermes-2-Yi-34B-pruned50"", sparsity=""sparse_w16a16"")
+prompt = ""How to make banana bread?""
+formatted_prompt =  f""<|im_start|>User:{prompt}\n<|im_start|>assistant:\n""
+
+sampling_params = SamplingParams(max_tokens=100, temperature=0)
+outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)
+""""""
+To make banana bread, you will need the following ingredients:
+
+Ingredients:
+- 2 ripe bananas
+- 1 cup all-purpose flour
+- 1/2 cup sugar
+- 1/2 cup butter
+- 1 teaspoon baking soda
+- 1 teaspoon baking powder
+- 1/2 teaspoon salt
+- 1/2 cup milk
+- 1 teaspoon vanilla extract
+
+Instructions:
+1. Preheat the oven to 3
+""""""
+```
+
+## Prompt template
+
+```
+<|im_start|>User:{prompt}
+<|im_start|>assistant:
+
+```
+
+## Sparsification
+For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below.
+
+Install [SparseML](https://github.com/neuralmagic/sparseml):
+```bash
+git clone https://github.com/neuralmagic/sparseml
+pip install -e ""sparseml[transformers]""
+```
+
+Replace the recipe as you like and run this one-shot compression script to apply SparseGPT:
+```python
+import sparseml.transformers
+
+original_model_name = ""NousResearch/Nous-Hermes-2-Yi-34B""
+calibration_dataset = ""open_platypus""
+output_directory = ""output/""
+
+recipe = """"""
+test_stage:
+  obcq_modifiers:
+    SparseGPTModifier:
+      sparsity: 0.5
+      sequential_update: true
+      mask_structure: 0:0
+      targets: ['re:model.layers.\d*$']
+""""""
+
+# Apply SparseGPT to the model
+sparseml.transformers.oneshot(
+    model=original_model_name,
+    dataset=calibration_dataset,
+    recipe=recipe,
+    output_dir=output_directory,
+)
+```
+
+## Slack
+
+For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)","{""id"": ""RedHatAI/Nous-Hermes-2-Yi-34B-pruned50"", ""author"": ""RedHatAI"", ""sha"": ""c0b5f32528b91b355dd0eaaefb7f32de9a2ecca6"", ""last_modified"": ""2024-03-05 14:03:16+00:00"", ""created_at"": ""2024-01-31 09:43:24+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 11, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""llama"", ""text-generation"", ""nm-vllm"", ""sparse"", ""conversational"", ""arxiv:2301.00774"", ""base_model:NousResearch/Nous-Hermes-2-Yi-34B"", ""base_model:finetune:NousResearch/Nous-Hermes-2-Yi-34B"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: NousResearch/Nous-Hermes-2-Yi-34B\ntags:\n- nm-vllm\n- sparse\ninference: true\nmodel_type: llama\nquantized_by: mgoin"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": ""<|startoftext|>"", ""chat_template"": ""{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<unk>"", ""unk_token"": ""<unk>"", ""use_default_system_prompt"": false}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00003-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00004-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00005-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00006-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00007-of-00007.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='recipe.yaml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.model', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-03-05 14:03:16+00:00"", ""cardData"": ""base_model: NousResearch/Nous-Hermes-2-Yi-34B\ntags:\n- nm-vllm\n- sparse\ninference: true\nmodel_type: llama\nquantized_by: mgoin"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""65ba163caefe7aac1ac24bbe"", ""modelId"": ""RedHatAI/Nous-Hermes-2-Yi-34B-pruned50"", ""usedStorage"": 137556962657}",1,,0,,0,https://huggingface.co/mradermacher/Nous-Hermes-2-Yi-34B-pruned50-GGUF,1,,0,huggingface/InferenceSupport/discussions/new?title=RedHatAI/Nous-Hermes-2-Yi-34B-pruned50&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BRedHatAI%2FNous-Hermes-2-Yi-34B-pruned50%5D(%2FRedHatAI%2FNous-Hermes-2-Yi-34B-pruned50)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Open-Sora_finetunes_20250426_221535.csv_finetunes_20250426_221535.csv

@@ -0,0 +1,32 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+hpcai-tech/Open-Sora,"---
+license: apache-2.0
+---
+
+We recommend you to use this weights in the [Open-Sora codebase]((https://github.com/hpcaitech/Open-Sora)).
+
+## 📰 News
+- **[2024.06.17]** 🔥 We released **Open-Sora 1.2**, which includes **3D-VAE**, **rectified flow**, and **score condition**.
+[[blog]](https://hpc-ai.com/blog/open-sora-from-hpc-ai-tech-team-continues-open-source-generate-any-16-second-720p-hd-video-with-one-click-model-weights-ready-to-use)
+* **[2024.03.18]** 🔥 We release **Open-Sora 1.0**, a fully open-source project for video generation.
+Open-Sora 1.0 supports a full pipeline of video data preprocessing, training with
+[ColossalAI](https://github.com/hpcaitech/ColossalAI) acceleration,
+inference, and more. Our provided checkpoints can produce 2s 512x512 videos with only 3 days training.
+[[blog]](https://hpc-ai.com/blog/open-sora-v1.0)
+* **[2024.03.04]** Open-Sora provides training with 46% cost reduction.
+[[blog]](https://hpc-ai.com/blog/open-sora)
+
+
+## Open-Sora: Democratizing Efficient Video Production for All
+We present [Open-Sora](https://github.com/hpcaitech/Open-Sora), an initiative dedicated to **efficiently** produce high-quality video and make the model, 
+tools and contents accessible to all. By embracing **open-source** principles, 
+Open-Sora not only democratizes access to advanced video generation techniques, but also offers a 
+streamlined and user-friendly platform that simplifies the complexities of video production.
+With Open-Sora, we aim to inspire innovation, creativity, and inclusivity in the realm of content creation. 
+
+<h4>Open-Sora is still at an early stage and under active development.</h4> 
+
+More details can be founded at [Open-Sora GitHub](https://github.com/hpcaitech/Open-Sora).
+
+
+","{""id"": ""hpcai-tech/Open-Sora"", ""author"": ""hpcai-tech"", ""sha"": ""b7e034189c6ac2d868bc8c672b7cdbd9c6b7ba96"", ""last_modified"": ""2024-06-21 07:57:53+00:00"", ""created_at"": ""2024-03-17 14:04:12+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 185, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: apache-2.0"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='OpenSora-v1-16x256x256.pth', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='OpenSora-v1-HQ-16x256x256.pth', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='OpenSora-v1-HQ-16x512x512.pth', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)""], ""spaces"": [""kadirnar/Open-Sora"", ""sandeshrajx/Open-Sora"", ""Taf2023/Open-Sora"", ""denizalpman/Open-Sora""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-06-21 07:57:53+00:00"", ""cardData"": ""license: apache-2.0"", ""transformersInfo"": null, ""_id"": ""65f6f85cd64a62e375871e2f"", ""modelId"": ""hpcai-tech/Open-Sora"", ""usedStorage"": 9129178554}",0,,0,,0,,0,,0,"Taf2023/Open-Sora, denizalpman/Open-Sora, huggingface/InferenceSupport/discussions/new?title=hpcai-tech/Open-Sora&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bhpcai-tech%2FOpen-Sora%5D(%2Fhpcai-tech%2FOpen-Sora)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kadirnar/Open-Sora, sandeshrajx/Open-Sora",5

Phi-3-small-128k-instruct_finetunes_20250427_003734.csv_finetunes_20250427_003734.csv

@@ -0,0 +1,715 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+microsoft/Phi-3-small-128k-instruct,"---
+license: mit
+license_link: https://huggingface.co/microsoft/Phi-3-small-128k-instruct/resolve/main/LICENSE
+
+language:
+- multilingual
+pipeline_tag: text-generation
+tags:
+- nlp
+- code
+inference:
+  parameters:
+    temperature: 0.7
+widget:
+  - messages:
+      - role: user
+        content: Can you provide ways to eat combinations of bananas and dragonfruits?
+---
+🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
+
+## Model Summary
+
+The Phi-3-Small-128K-Instruct is a 7B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.
+The model belongs to the Phi-3 family with the Small version in two variants [8K](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) which is the context length (in tokens) that it can support.
+
+The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.
+When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3-Small-128K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up.
+
+Resources and Technical Documentation:
+
++ [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024)
++ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
++ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai)
++ [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook)
+
+|         | Short Context | Long Context |
+| ------- | ------------- | ------------ |
+| Mini    | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
+| Small   | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
+| Medium  | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
+| Vision  |  | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
+
+
+## Intended Uses
+
+**Primary use cases**
+
+The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications which require :
+
+1) Memory/compute constrained environments
+2) Latency bound scenarios
+3) Strong reasoning (especially code, math and logic)
+
+Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. 
+
+**Use case considerations**
+
+Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
+
+Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.  
+
+## How to Use
+
+Phi-3-Small-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
+* Install tiktoken (0.6.0) ans triton (2.3.0)
+
+* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
+
+* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
+
+The current `transformers` version can be verified with: `pip list | grep transformers`.
+
+Phi-3-Small-128K-Instruct is also available in [Azure AI](https://ai.azure.com/explore/models?&selectedCollection=phi).
+
+### Tokenizer
+
+Phi-3-Small-128K-Instruct supports a vocabulary size of up to `100352` tokens.
+
+### Chat Format
+
+Given the nature of the training data, the Phi-3-Small-128K-Instruct model is best suited for prompts using the chat format as follows. 
+You can provide the prompt as a question with a generic template as follow:
+```markdown
+<|endoftext|><|user|>\nQuestion <|end|>\n<|assistant|>
+```
+For example:
+```markdown
+<|endoftext|><|user|>
+How to explain Internet for a medieval knight?<|end|>
+<|assistant|>
+```
+
+where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following:
+
+```markdown
+<|endoftext|><|user|>
+I am going to Paris, what should I see?<|end|>
+<|assistant|>
+Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world.""<|end|>
+<|user|>
+What is so great about #1?<|end|>
+<|assistant|>
+```
+
+### Sample inference code
+
+This code snippets show how to get quickly started with running the model on a GPU:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+torch.random.manual_seed(0)
+model_id = ""microsoft/Phi-3-small-128k-instruct""
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=""auto"", 
+    trust_remote_code=True, 
+)
+assert torch.cuda.is_available(), ""This model needs a GPU to run ...""
+device = torch.cuda.current_device()
+model = model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {""role"": ""user"", ""content"": ""Can you provide ways to eat combinations of bananas and dragonfruits?""},
+    {""role"": ""assistant"", ""content"": ""Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.""},
+    {""role"": ""user"", ""content"": ""What about solving an 2x + 3 = 7 equation?""},
+]
+
+pipe = pipeline(
+    ""text-generation"",
+    model=model,
+    tokenizer=tokenizer,
+    device=device
+)
+
+generation_args = {
+    ""max_new_tokens"": 500,
+    ""return_full_text"": False,
+    ""temperature"": 0.0,
+    ""do_sample"": False,
+}
+
+output = pipe(messages, **generation_args)
+print(output[0]['generated_text'])
+```
+
+*Some applications/frameworks might not include a BOS token (`<|endoftext|>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.*
+
+## Responsible AI Considerations
+
+Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+
++ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.   
++ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. 
++ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. 
++ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.  
++ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as ""typing, math, random, collections, datetime, itertools"". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.   
+
+Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+
++ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
++ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. 
++ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).   
++ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. 
++ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
+
+
+## Training
+
+### Model
+
+* Architecture: Phi-3 Small-128K-Instruct has 7B parameters and is a dense decoder-only Transformer model with alternating dense and blocksparse attentions. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
+* Inputs: Text. It is best suited for prompts using chat format.
+* Context length: 128K tokens
+* GPUs: 1024 H100-80G
+* Training time: 18 days
+* Training data: 4.8T tokens
+* Outputs: Generated text in response to the input
+* Dates: Our models were trained between February and April 2024
+* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.
+* Release dates	The model weight is released on May 21, 2024.
+
+### Datasets
+
+Our training data includes a wide variety of sources, totaling 4.8 trillion tokens (including 10% multilingual), and is a combination of 
+1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 
+2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 
+3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
+
+We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. As an example, the result of a game in premier league in a particular day might be good training data for frontier models, but we need to remove such information to leave more model capacity for reasoning for the small size models. More details about data can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
+
+## Benchmarks
+
+We report the results for Phi-3-Small-128K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Mixtral-8x7b, Gemini-Pro, Gemma 7B, Llama-3-8B-Instruct, GPT-3.5-Turbo-1106, and GPT-4-Turbo-1106.
+
+All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.
+
+As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. 
+The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
+More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
+
+The number of k–shot examples is listed per-benchmark. 
+
+|Benchmark|Phi-3-Small-128K-Instruct<br>7b|Gemma<br>7B|Mixtral<br>8x7B|Llama-3-Instruct<br>8b|GPT-3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)|
+|---------|-------------------------------|----------|-------------|-------------------------|---------------------------|------------|--------------------------------|
+|AGI Eval<br>5-shot|43.9|42.1|45.2|42.0|48.4|49.0|59.6|
+|MMLU<br>5-shot|75.5|63.6|70.5|66.5|71.4|66.7|84.0|
+|BigBench Hard<br>3-shot|77.6|59.6|69.7|51.5|68.3|75.6|87.7|
+|ANLI<br>7-shot|55.8|48.7|55.2|57.3|58.1|64.2|71.7|
+|HellaSwag<br>5-shot|79.6|49.8|70.4|71.1|78.8|76.2|88.3|
+|ARC Challenge<br>10-shot|90.8|78.3|87.3|82.8|87.4|88.3|95.6|
+|ARC Easy<br>10-shot|97.3|91.4|95.6|93.4|96.3|96.1|98.8|
+|BoolQ<br>2-shot|83.7|66.0|76.6|80.9|79.1|86.4|91.3|
+|CommonsenseQA<br>10-shot|80.8|76.2|78.1|79.0|79.6|81.8|86.7|
+|MedQA<br>2-shot|46.3|49.6|62.2|60.5|63.4|58.2|83.7|
+|OpenBookQA<br>10-shot|87.8|78.6|85.8|82.6|86.0|86.4|93.4|
+|PIQA<br>5-shot|88.1|78.1|86.0|75.7|86.6|86.2|90.1|
+|Social IQA<br>5-shot|78.7|65.5|75.9|73.9|68.3|75.4|81.7|
+|TruthfulQA (MC2)<br>10-shot|69.6|52.1|60.1|63.2|67.7|72.6|85.2|
+|WinoGrande<br>5-shot|80.1|55.6|62.0|65.0|68.8|72.2|86.7|
+|TriviaQA<br>5-shot|66.0|72.3|82.2|67.7|85.8|80.2|73.3|
+|GSM8K Chain of Thought<br>8-shot|87.3|59.8|64.7|77.4|78.1|80.4|94.2|
+|HumanEval<br>0-shot|59.1|34.1|37.8|60.4|62.2|64.4|79.9|
+|MBPP<br>3-shot|70.3|51.5|60.2|67.7|77.8|73.2|86.7|
+|Average|74.6|61.8|69.8|69.4|74.3|75.4|85.2|
+
+We take a closer look at different categories across 80 public benchmark datasets at the table below:
+
+|Benchmark|Phi-3-Small-128K-Instruct<br>7b|Gemma<br>7B|Mixtral<br>8x7B|Llama-3-Instruct<br>8b|GPT-3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)|
+|--------|--------------------------|--------|-------------|-------------------|-------------------|----------|------------------------|
+|Popular aggregated benchmark|70.6|59.4|66.2|59.9|67.0|67.5|80.5|
+|Reasoning|80.3|69.1|77.0|75.7|78.3|80.4|89.3|
+|Language understanding|67.4|58.4|64.9|65.4|70.4|75.3|81.6|
+|Code generation|60.0|45.6|52.7|56.4|70.4|66.7|76.1|
+|Math|48.1|35.8|40.3|41.1|52.8|50.9|67.1|
+|Factual knowledge|41.7|46.7|58.6|43.1|63.4|54.6|45.9|
+|Multilingual|62.6|63.2|63.4|65.0|69.1|76.5|82.0|
+|Robustness|68.7|38.4|51.0|64.5|69.3|69.7|84.6|
+
+
+## Software
+
+* [PyTorch](https://github.com/pytorch/pytorch)
+* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
+* [Transformers](https://github.com/huggingface/transformers)
+* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
+* [Tiktoken](https://github.com/openai/tiktoken)
+* [Triton](https://github.com/openai/triton)
+
+## Hardware
+Note that by default, the Phi-3-Small model uses flash attention 2 and Triton blocksparse attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
+* NVIDIA A100
+* NVIDIA A6000
+* NVIDIA H100
+
+If you want to run the model on:
++ Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [128K](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)
+
+
+## Cross Platform Support
+
+ONNX runtime ecosystem now supports Phi3 small models  across platforms and hardware. 
+Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA).  
+Along with DML, ONNX Runtime provides cross platform support for Phi3 Small  across a range of devices CPU, GPU, and mobile. 
+Here are some of the optimized configurations we have added: 
+
+1. ONNX models for int4 DML: Quantized to int4 via AWQ
+2. ONNX model for fp16 CUDA
+3. ONNX model for int4 CUDA: Quantized to int4 via RTN
+4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN
+
+## License
+
+The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-small-128k/resolve/main/LICENSE).
+
+## Trademarks
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
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+PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: microsoft/Phi-3-small-128k-instruct
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with hqq.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo microsoft/Phi-3-small-128k-instruct installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install hqq
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+    from hqq.engine.hf import HQQModelForCausalLM
+ from hqq.models.hf.base import AutoHQQHFModel
+
+   try:
+     model = HQQModelForCausalLM.from_quantized(""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed"", device_map='auto')
+    except: 
+     model = AutoHQQHFModel.from_quantized(""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed"")
+   tokenizer = AutoTokenizer.from_pretrained(""microsoft/Phi-3-small-128k-instruct"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model microsoft/Phi-3-small-128k-instruct before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""b92d679634c0ea2189073377ee397f294f8f0228"", ""last_modified"": ""2024-07-15 20:30:37+00:00"", ""created_at"": ""2024-07-15 20:29:38+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""phi3small"", ""text-generation"", ""pruna-ai"", ""conversational"", ""custom_code"", ""base_model:microsoft/Phi-3-small-128k-instruct"", ""base_model:finetune:microsoft/Phi-3-small-128k-instruct"", ""autotrain_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""Phi3SmallForCausalLM""], ""auto_map"": {""AutoConfig"": ""microsoft/Phi-3-small-128k-instruct--configuration_phi3_small.Phi3SmallConfig"", ""AutoModelForCausalLM"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""AutoTokenizer"": ""microsoft/Phi-3-small-128k-instruct--tokenization_phi3_small.Phi3SmallTokenizer""}, ""model_type"": ""phi3small"", ""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""chat_template"": ""{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='cl100k_base.tiktoken', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='qmodel.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-07-15 20:30:37+00:00"", ""cardData"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""_id"": ""669586b26206df6098a69a83"", ""modelId"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed"", ""usedStorage"": 1862171173}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed%5D(%2FPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-HQQ-1bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: microsoft/Phi-3-small-128k-instruct
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with hqq.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo microsoft/Phi-3-small-128k-instruct installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install hqq
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+    from hqq.engine.hf import HQQModelForCausalLM
+ from hqq.models.hf.base import AutoHQQHFModel
+
+   try:
+     model = HQQModelForCausalLM.from_quantized(""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed"", device_map='auto')
+    except: 
+     model = AutoHQQHFModel.from_quantized(""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed"")
+   tokenizer = AutoTokenizer.from_pretrained(""microsoft/Phi-3-small-128k-instruct"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model microsoft/Phi-3-small-128k-instruct before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""6f419e8a4a863e99f80016d20a4850c0afb21ec1"", ""last_modified"": ""2024-07-15 20:31:14+00:00"", ""created_at"": ""2024-07-15 20:29:42+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 3, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""phi3small"", ""text-generation"", ""pruna-ai"", ""conversational"", ""custom_code"", ""base_model:microsoft/Phi-3-small-128k-instruct"", ""base_model:finetune:microsoft/Phi-3-small-128k-instruct"", ""autotrain_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""Phi3SmallForCausalLM""], ""auto_map"": {""AutoConfig"": ""microsoft/Phi-3-small-128k-instruct--configuration_phi3_small.Phi3SmallConfig"", ""AutoModelForCausalLM"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""AutoTokenizer"": ""microsoft/Phi-3-small-128k-instruct--tokenization_phi3_small.Phi3SmallTokenizer""}, ""model_type"": ""phi3small"", ""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""chat_template"": ""{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='cl100k_base.tiktoken', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='qmodel.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-07-15 20:31:14+00:00"", ""cardData"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""microsoft/Phi-3-small-128k-instruct--modeling_phi3_small.Phi3SmallForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""_id"": ""669586b68b906774be07e503"", ""modelId"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed"", ""usedStorage"": 2734586405}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/microsoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed%5D(%2FPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-HQQ-2bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: microsoft/Phi-3-small-128k-instruct
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with quanto.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo microsoft/Phi-3-small-128k-instruct installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install quanto
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   IMPORTS
+
+   model = AutoModelForCausalLM.from_pretrained(""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed"", trust_remote_code=True, device_map='auto')
+   tokenizer = AutoTokenizer.from_pretrained(""microsoft/Phi-3-small-128k-instruct"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model microsoft/Phi-3-small-128k-instruct before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""60fb3974293833d7aba210b4cfbdbc44c381e53f"", ""last_modified"": ""2024-07-19 09:28:06+00:00"", ""created_at"": ""2024-07-15 20:35:46+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pruna-ai"", ""base_model:microsoft/Phi-3-small-128k-instruct"", ""base_model:finetune:microsoft/Phi-3-small-128k-instruct"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": null, ""model_index"": null, ""config"": {""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""chat_template"": ""{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='cl100k_base.tiktoken', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-07-19 09:28:06+00:00"", ""cardData"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""66958822534f204a2b66354d"", ""modelId"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed"", ""usedStorage"": 15623850318}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed%5D(%2FPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-int8bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: microsoft/Phi-3-small-128k-instruct
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with quanto.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo microsoft/Phi-3-small-128k-instruct installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install quanto
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   IMPORTS
+
+   model = AutoModelForCausalLM.from_pretrained(""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed"", trust_remote_code=True, device_map='auto')
+   tokenizer = AutoTokenizer.from_pretrained(""microsoft/Phi-3-small-128k-instruct"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model microsoft/Phi-3-small-128k-instruct before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""558afb26200590f7f1d1e1a17b626d80adea880f"", ""last_modified"": ""2024-07-19 09:21:11+00:00"", ""created_at"": ""2024-07-15 20:36:09+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pruna-ai"", ""base_model:microsoft/Phi-3-small-128k-instruct"", ""base_model:finetune:microsoft/Phi-3-small-128k-instruct"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": null, ""model_index"": null, ""config"": {""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""chat_template"": ""{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='cl100k_base.tiktoken', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-07-19 09:21:11+00:00"", ""cardData"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""66958839bb17842a4bbfe393"", ""modelId"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed"", ""usedStorage"": 15623850318}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed%5D(%2FPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-float8bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed,"---
+thumbnail: ""https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg""
+base_model: microsoft/Phi-3-small-128k-instruct
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+    <a href=""https://www.pruna.ai/"" target=""_blank"" rel=""noopener noreferrer"">
+        <img src=""https://i.imgur.com/eDAlcgk.png"" alt=""PrunaAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with quanto.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append ""turbo"", ""tiny"", or ""green"" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are ""first"" metrics?*** Results mentioning ""first"" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are ""Sync"" and ""Async"" metrics?*** ""Sync"" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. ""Async"" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo microsoft/Phi-3-small-128k-instruct installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install quanto
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   IMPORTS
+
+   model = AutoModelForCausalLM.from_pretrained(""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed"", trust_remote_code=True, device_map='auto')
+   tokenizer = AutoTokenizer.from_pretrained(""microsoft/Phi-3-small-128k-instruct"")
+    
+   input_ids = tokenizer(""What is the color of prunes?,"", return_tensors='pt').to(model.device)[""input_ids""]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model microsoft/Phi-3-small-128k-instruct before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).","{""id"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed"", ""author"": ""PrunaAI"", ""sha"": ""71a3f53d6f1fbba105de178ddc6fb66322258378"", ""last_modified"": ""2024-07-19 09:20:03+00:00"", ""created_at"": ""2024-07-15 20:36:14+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pruna-ai"", ""base_model:microsoft/Phi-3-small-128k-instruct"", ""base_model:finetune:microsoft/Phi-3-small-128k-instruct"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""widget_data"": null, ""model_index"": null, ""config"": {""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""chat_template"": ""{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='cl100k_base.tiktoken', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.pt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='smash_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-07-19 09:20:03+00:00"", ""cardData"": ""base_model: microsoft/Phi-3-small-128k-instruct\nmetrics:\n- memory_disk\n- memory_inference\n- inference_latency\n- inference_throughput\n- inference_CO2_emissions\n- inference_energy_consumption\ntags:\n- pruna-ai\nthumbnail: https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""6695883e69d46521da9ff2e9"", ""modelId"": ""PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed"", ""usedStorage"": 15623850446}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=PrunaAI/microsoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed%5D(%2FPrunaAI%2Fmicrosoft-Phi-3-small-128k-instruct-QUANTO-int4bit-smashed)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

Ruyi-Mini-7B_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,196 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+IamCreateAI/Ruyi-Mini-7B,"---
+language:
+ - ""en""
+tags:
+ - video generation
+ - CreateAI
+license: apache-2.0
+pipeline_tag: image-to-video
+---
+
+
+# Ruyi-Mini-7B
+[Hugging Face](https://huggingface.co/IamCreateAI/Ruyi-Mini-7B) | [Github](https://github.com/IamCreateAI/Ruyi-Models)
+
+An image-to-video model by CreateAI.
+
+## Overview
+
+Ruyi-Mini-7B is an open-source image-to-video generation model. Starting with an input image, Ruyi produces subsequent video frames at resolutions ranging from 360p to 720p, supporting various aspect ratios and a maximum duration of 5 seconds. Enhanced with motion and camera control, Ruyi offers greater flexibility and creativity in video generation. We are releasing the model under the permissive Apache 2.0 license.
+
+## Update
+
+Dec 24, 2024: The diffusion model is updated to fix the black lines when creating 3:4 or 4:5 videos. 
+
+Dec 16, 2024: Ruyi-mini-7B is released. 
+
+## Installation
+
+Install code from github:
+```bash
+git clone https://github.com/IamCreateAI/Ruyi-Models
+cd Ruyi-Models
+pip install -r requirements.txt
+```
+
+## Running
+
+We provide two ways to run our model. The first is directly using python code.
+
+```bash
+python3 predict_i2v.py
+```
+
+Or use ComfyUI wrapper in our [github repo](https://github.com/IamCreateAI/Ruyi-Models).
+
+## Model Architecture
+
+Ruyi-Mini-7B is an advanced image-to-video model with about 7.1 billion parameters. The model architecture is modified from [EasyAnimate V4 model](https://github.com/aigc-apps/EasyAnimate), whose transformer module is inherited from [HunyuanDiT](https://github.com/Tencent/HunyuanDiT). It comprises three key components:
+  1. Casual VAE Module: Handles video compression and decompression. It reduces spatial resolution to 1/8 and temporal resolution to 1/4, with each latent pixel is represented in 16 float numbers after compression.
+  2. Diffusion Transformer Module: Generates compressed video data using 3D full attention, with: 
+  - 2D Normalized-RoPE for spatial dimensions;
+  - Sin-cos position embedding for temporal dimensions;
+  - DDPM (Denoising Diffusion Probabilistic Models) for model training.
+  3. Ruyi also utilizes a CLIP model to extract the semantic features from the input image to guide the whole video generation. The CLIP features are introduced into the transformer by cross-attention.
+
+## Training Data and Methodology
+  The training process is divided into four phases:
+  - Phase 1: Pre-training from scratch with ~200M video clips and ~30M images at a 256-resolution, using a batch size of 4096 for 350,000 iterations to achieve full convergence.
+  - Phase 2: Fine-tuning with ~60M video clips for multi-scale resolutions (384–512), with a batch size of 1024 for 60,000 iterations.
+  - Phase 3: High-quality fine-tuning with ~20M video clips and ~8M images for 384–1024 resolutions, with dynamic batch sizes based on memory and 10,000 iterations.
+  - Phase 4: Image-to-video training with ~10M curated high-quality video clips, with dynamic batch sizes based on memory for ~10,000 iterations.
+
+## Hardware Requirements
+The VRAM cost of Ruyi depends on the resolution and duration of the video. Here we list the costs for some typical video size. Tested on single A100.
+|Video Size | 360x480x120 | 384x672x120 | 480x640x120 | 630x1120x120 | 720x1280x120 | 
+|:--:|:--:|:--:|:--:|:--:|:--:|
+|Memory   | 21.5GB   | 25.5GB   | 27.7GB   | 44.9GB   |   54.8GB   |
+|Time     | 03:10   | 05:29   | 06:49   | 24:18   |   39:02   |
+
+For 24GB VRAM cards such as RTX4090, we provide `low_gpu_memory_mode`, under which the model can generate 720x1280x120 videos with a longer time.
+
+## Showcase
+
+### Image to Video Effects
+
+<table border=""0"" style=""width: 100%; text-align: left; margin-top: 20px;"">
+    <tr>
+        <td><video src=""https://github.com/user-attachments/assets/4dedf40b-82f2-454c-9a67-5f4ed243f5ea"" width=""100%"" style=""max-height:640px; min-height: 200px"" controls autoplay loop></video></td>
+        <td><video src=""https://github.com/user-attachments/assets/905fef17-8c5d-49b0-a49a-6ae7e212fa07"" width=""100%"" style=""max-height:640px; min-height: 200px"" controls autoplay loop></video></td>
+        <td><video src=""https://github.com/user-attachments/assets/20daab12-b510-448a-9491-389d7bdbbf2e"" width=""100%"" style=""max-height:640px; min-height: 200px"" controls autoplay loop></video></td>
+        <td><video src=""https://github.com/user-attachments/assets/f1bb0a91-d52a-4611-bac2-8fcf9658cac0"" width=""100%"" style=""max-height:640px; min-height: 200px"" controls autoplay loop></video></td>
+    </tr>
+</table>
+
+### Camera Control
+
+<table border=""0"" style=""width: 100%; text-align: center; "">
+    <tr>
+        <td align=center><img src=""https://github.com/user-attachments/assets/8aedcea6-3b8e-4c8b-9fed-9ceca4d41954"" width=""100%"" style=""max-height:240px; min-height: 100px; margin-top: 20%;""></img></td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/d9d027d4-0d4f-45f5-9d46-49860b562c69"" width=""100%"" style=""max-height:360px; min-height: 200px"" controls autoplay loop></video></td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/7716a67b-1bb8-4d44-b128-346cbc35e4ee"" width=""100%"" style=""max-height:360px; min-height: 200px"" controls autoplay loop></video></td>
+    </tr>
+    <tr><td>input</td><td>left</td><td>right</td></tr>
+    <tr>
+        <td align=center><video src=""https://github.com/user-attachments/assets/cc1f1928-cab7-4c4b-90af-928936102e66"" width=""100%"" style=""max-height:360px; min-height: 200px"" controls autoplay loop></video></td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/c742ea2c-503a-454f-a61a-10b539100cd9"" width=""100%"" style=""max-height:360px; min-height: 200px"" controls autoplay loop></video></td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/442839fa-cc53-4b75-b015-909e44c065e0"" width=""100%"" style=""max-height:360px; min-height: 200px"" controls autoplay loop></video></td>
+    </tr>
+    <tr><td>static</td><td>up</td><td>down</td></tr>
+</table>
+
+### Motion Amplitude Control
+
+<table border=""0"" style=""width: 100%; text-align: left; margin-top: 20px;"">
+    <tr>
+        <td align=center><video src=""https://github.com/user-attachments/assets/0020bd54-0ff6-46ad-91ee-d9f0df013772"" width=""100%"" controls autoplay loop></video>motion 1</td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/d1c26419-54e3-4b86-8ae3-98e12de3022e"" width=""100%"" controls autoplay loop></video>motion 2</td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/535147a2-049a-4afc-8d2a-017bc778977e"" width=""100%"" controls autoplay loop></video>motion 3</td>
+        <td align=center><video src=""https://github.com/user-attachments/assets/bf893d53-2e11-406f-bb9a-2aacffcecd44"" width=""100%"" controls autoplay loop></video>motion 4</td>
+    </tr>
+</table>
+
+## Limitations
+There are some known limitations in this experimental release. Texts, hands and crowded human faces may be distorted. The video may cut to another scene when the model does not know how to generate future frames. We are still working on these problems and will update the model as we make progress.
+
+
+## BibTeX
+```
+@misc{createai2024ruyi,
+      title={Ruyi-Mini-7B},
+      author={CreateAI Team},
+      year={2024},
+      publisher = {GitHub},
+      journal = {GitHub repository},
+      howpublished={\url{https://github.com/IamCreateAI/Ruyi-Models}}
+}
+```
+
+## Contact Us
+
+You are welcomed to join our [Discord](https://discord.com/invite/nueQFQwwGw) or Wechat Group (Scan QR code to add Ruyi Assistant and join the official group) for further discussion!
+
+![wechat](https://github.com/user-attachments/assets/cc5e25c6-34ab-4be1-a59b-7d5789264a9c)","{""id"": ""IamCreateAI/Ruyi-Mini-7B"", ""author"": ""IamCreateAI"", ""sha"": ""fbb88130fc98dbc2e2de2ad510a081e332535566"", ""last_modified"": ""2024-12-25 10:50:32+00:00"", ""created_at"": ""2024-12-16 12:54:19+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 483, ""downloads_all_time"": null, ""likes"": 609, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""safetensors"", ""video generation"", ""CreateAI"", ""image-to-video"", ""en"", ""license:apache-2.0"", ""diffusers:RuyiInpaintPipeline"", ""region:us""], ""pipeline_tag"": ""image-to-video"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\nlicense: apache-2.0\npipeline_tag: image-to-video\ntags:\n- video generation\n- CreateAI"", ""widget_data"": null, ""model_index"": null, ""config"": {""diffusers"": {""_class_name"": ""RuyiInpaintPipeline""}}, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='embeddings.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='image_encoder/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='image_encoder/preprocessor_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='image_encoder/pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model_index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='scheduler/scheduler_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='transformer/diffusion_pytorch_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/diffusion_pytorch_model.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [""NaqchoAli/testimage""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-12-25 10:50:32+00:00"", ""cardData"": ""language:\n- en\nlicense: apache-2.0\npipeline_tag: image-to-video\ntags:\n- video generation\n- CreateAI"", ""transformersInfo"": null, ""_id"": ""676022fba63fff7b5bfdc8be"", ""modelId"": ""IamCreateAI/Ruyi-Mini-7B"", ""usedStorage"": 17334852644}",0,"https://huggingface.co/tcoh/CoralAI, https://huggingface.co/stargolf/mon_chat, https://huggingface.co/kabirclark/kabirworld, https://huggingface.co/genoxan/renax",4,https://huggingface.co/S4lv4tr0n/Mandraken,1,,0,,0,"NaqchoAli/testimage, huggingface/InferenceSupport/discussions/new?title=IamCreateAI/Ruyi-Mini-7B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BIamCreateAI%2FRuyi-Mini-7B%5D(%2FIamCreateAI%2FRuyi-Mini-7B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A",2
+tcoh/CoralAI,"---
+license: mit
+datasets:
+- fka/awesome-chatgpt-prompts
+base_model:
+- Qwen/QwQ-32B-Preview
+- IamCreateAI/Ruyi-Mini-7B
+new_version: Qwen/Qwen2.5-Coder-32B-Instruct
+library_name: fasttext
+metrics:
+- accuracy
+---","{""id"": ""tcoh/CoralAI"", ""author"": ""tcoh"", ""sha"": ""be35b5945b0ce135373cbe6952b62abac630b084"", ""last_modified"": ""2024-12-23 09:48:49+00:00"", ""created_at"": ""2024-12-23 09:41:52+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""fasttext"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""fasttext"", ""dataset:fka/awesome-chatgpt-prompts"", ""base_model:IamCreateAI/Ruyi-Mini-7B"", ""base_model:finetune:IamCreateAI/Ruyi-Mini-7B"", ""license:mit"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- Qwen/QwQ-32B-Preview\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- fka/awesome-chatgpt-prompts\nlibrary_name: fasttext\nlicense: mit\nmetrics:\n- accuracy\nnew_version: Qwen/Qwen2.5-Coder-32B-Instruct"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-12-23 09:48:49+00:00"", ""cardData"": ""base_model:\n- Qwen/QwQ-32B-Preview\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- fka/awesome-chatgpt-prompts\nlibrary_name: fasttext\nlicense: mit\nmetrics:\n- accuracy\nnew_version: Qwen/Qwen2.5-Coder-32B-Instruct"", ""transformersInfo"": null, ""_id"": ""67693060a5bdfcf3b312f2c8"", ""modelId"": ""tcoh/CoralAI"", ""usedStorage"": 0}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=tcoh/CoralAI&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Btcoh%2FCoralAI%5D(%2Ftcoh%2FCoralAI)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+stargolf/mon_chat,"---
+license: apache-2.0
+datasets:
+- fka/awesome-chatgpt-prompts
+language:
+- fr
+base_model:
+- IamCreateAI/Ruyi-Mini-7B
+---","{""id"": ""stargolf/mon_chat"", ""author"": ""stargolf"", ""sha"": ""4eb82fea70247c0498623dcde76c2d951ef1e55b"", ""last_modified"": ""2024-12-29 21:13:42+00:00"", ""created_at"": ""2024-12-29 21:12:24+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""fr"", ""dataset:fka/awesome-chatgpt-prompts"", ""base_model:IamCreateAI/Ruyi-Mini-7B"", ""base_model:finetune:IamCreateAI/Ruyi-Mini-7B"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- fka/awesome-chatgpt-prompts\nlanguage:\n- fr\nlicense: apache-2.0"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-12-29 21:13:42+00:00"", ""cardData"": ""base_model:\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- fka/awesome-chatgpt-prompts\nlanguage:\n- fr\nlicense: apache-2.0"", ""transformersInfo"": null, ""_id"": ""6771bb3879d1ea02e20531d1"", ""modelId"": ""stargolf/mon_chat"", ""usedStorage"": 0}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=stargolf/mon_chat&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bstargolf%2Fmon_chat%5D(%2Fstargolf%2Fmon_chat)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+kabirclark/kabirworld,"---
+license: llama3.3
+datasets:
+- HuggingFaceFW/fineweb-2
+language:
+- aa
+metrics:
+- bleu
+base_model:
+- meta-llama/Llama-3.3-70B-Instruct
+- IamCreateAI/Ruyi-Mini-7B
+new_version: meta-llama/Llama-3.3-70B-Instruct
+pipeline_tag: text-generation
+library_name: asteroid
+tags:
+- code
+- legal
+---
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained(""black-forest-labs/FLUX.1-dev"")
+
+prompt = ""Astronaut in a jungle, cold color palette, muted colors, detailed, 8k""
+image = pipe(prompt).images[0]","{""id"": ""kabirclark/kabirworld"", ""author"": ""kabirclark"", ""sha"": ""d8fed2cc5eafb2864e89342839ca9ddb85cbd745"", ""last_modified"": ""2024-12-31 12:11:56+00:00"", ""created_at"": ""2024-12-31 12:08:32+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""asteroid"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""asteroid"", ""code"", ""legal"", ""text-generation"", ""aa"", ""dataset:HuggingFaceFW/fineweb-2"", ""base_model:IamCreateAI/Ruyi-Mini-7B"", ""base_model:finetune:IamCreateAI/Ruyi-Mini-7B"", ""license:llama3.3"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- meta-llama/Llama-3.3-70B-Instruct\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- HuggingFaceFW/fineweb-2\nlanguage:\n- aa\nlibrary_name: asteroid\nlicense: llama3.3\nmetrics:\n- bleu\npipeline_tag: text-generation\ntags:\n- code\n- legal\nnew_version: meta-llama/Llama-3.3-70B-Instruct"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-12-31 12:11:56+00:00"", ""cardData"": ""base_model:\n- meta-llama/Llama-3.3-70B-Instruct\n- IamCreateAI/Ruyi-Mini-7B\ndatasets:\n- HuggingFaceFW/fineweb-2\nlanguage:\n- aa\nlibrary_name: asteroid\nlicense: llama3.3\nmetrics:\n- bleu\npipeline_tag: text-generation\ntags:\n- code\n- legal\nnew_version: meta-llama/Llama-3.3-70B-Instruct"", ""transformersInfo"": null, ""_id"": ""6773dec0a2128da37adb090e"", ""modelId"": ""kabirclark/kabirworld"", ""usedStorage"": 0}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=kabirclark/kabirworld&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bkabirclark%2Fkabirworld%5D(%2Fkabirclark%2Fkabirworld)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+genoxan/renax,"---
+license: c-uda
+datasets:
+- argilla/FinePersonas-v0.1
+language:
+- ar
+metrics:
+- charcut_mt
+- bertscore
+base_model:
+- IamCreateAI/Ruyi-Mini-7B
+new_version: IamCreateAI/Ruyi-Mini-7B
+pipeline_tag: text-to-image
+library_name: diffusers
+tags:
+- biology
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SuperNova-Medius_finetunes_20250426_212347.csv_finetunes_20250426_212347.csv

@@ -0,0 +1,267 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+arcee-ai/SuperNova-Medius,"---
+license: apache-2.0
+library_name: transformers
+tags:
+- mergekit
+- merge
+base_model:
+- Qwen/Qwen2.5-14B
+model-index:
+- name: SuperNova-Medius
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 55.6
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 49.3
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 32.48
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 17.9
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 19.19
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 48.83
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius
+      name: Open LLM Leaderboard
+---
+
+# Arcee-SuperNova-Medius
+
+Arcee-SuperNova-Medius is a 14B parameter language model developed by Arcee.ai, built on the Qwen2.5-14B-Instruct architecture. This unique model is the result of a cross-architecture distillation pipeline, combining knowledge from both the Qwen2.5-72B-Instruct model and the Llama-3.1-405B-Instruct model. By leveraging the strengths of these two distinct architectures, SuperNova-Medius achieves high-quality instruction-following and complex reasoning capabilities in a mid-sized, resource-efficient form.
+
+SuperNova-Medius is designed to excel in a variety of business use cases, including customer support, content creation, and technical assistance, while maintaining compatibility with smaller hardware configurations. It’s an ideal solution for organizations looking for advanced capabilities without the high resource requirements of larger models like our SuperNova-70B.
+
+## Distillation Overview
+
+The development of SuperNova-Medius involved a sophisticated multi-teacher, cross-architecture distillation process, with the following key steps:
+
+1. **Logit Distillation from Llama 3.1 405B**:
+   - We distilled the logits of Llama 3.1 405B using an offline approach.
+   - The top K logits for each token were stored to capture most of the probability mass while managing storage requirements.
+
+2. **Cross-Architecture Adaptation**:
+   - Using `mergekit-tokensurgeon`, we created a version of Qwen2.5-14B that uses the vocabulary of Llama 3.1 405B.
+   - This allowed for the use of Llama 3.1 405B logits in training the Qwen-based model.
+
+3. **Distillation to Qwen Architecture**:
+   - The adapted Qwen2.5-14B model was trained using the stored 405B logits as the target.
+
+4. **Parallel Qwen Distillation**:
+   - In a separate process, Qwen2-72B was distilled into a 14B model.
+
+5. **Final Fusion and Fine-Tuning**:
+   - The Llama-distilled Qwen model's vocabulary was reverted to Qwen vocabulary.
+   - After re-aligning the vocabularies, a final fusion and fine-tuning step was conducted, using a specialized dataset from [EvolKit](https://github.com/arcee-ai/EvolKit) to ensure that SuperNova-Medius maintained coherence, fluency, and context understanding across a broad range of tasks.
+
+## Performance Evaluation
+
+Below are the benchmark results of SuperNova-Medius compared to similar models in its class:
+
+| Model | Average | IFEval | BBH | GPQA | MMLU Pro | MuSR | Math Level 5 |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Mistral-Small 2409 | 0.423 | 0.628 | 0.581 | 0.333 | 0.410 | 0.406 | 0.181 |
+| Supernova-Lite | 0.427 | 0.786 | 0.511 | 0.306 | 0.388 | 0.415 | 0.155 |
+| Qwen2.5-14B-Instruct | 0.450 | 0.827 | 0.623 | 0.358 | 0.490 | 0.403 | 0.000 |
+| Supernova-Medius | **0.480** | **0.832** | **0.631** | **0.359** | **0.502** | **0.402** | **0.152** |
+
+SuperNova-Medius performs exceptionally well in instruction-following (IFEval) and complex reasoning tasks (BBH), demonstrating its capability to handle a variety of real-world scenarios. It outperforms Qwen2.5-14B and SuperNova-Lite in multiple benchmarks, making it a powerful yet efficient choice for high-quality generative AI applications.
+
+## Model Use Cases
+
+Arcee-SuperNova-Medius is suitable for a range of applications, including:
+
+- **Customer Support**: With its robust instruction-following and dialogue management capabilities, SuperNova-Medius can handle complex customer interactions, reducing the need for human intervention.
+- **Content Creation**: The model’s advanced language understanding and generation abilities make it ideal for creating high-quality, coherent content across diverse domains.
+- **Technical Assistance**: SuperNova-Medius has a deep reservoir of technical knowledge, making it an excellent assistant for programming, technical documentation, and other expert-level content creation.
+
+## Deployment Options
+
+SuperNova-Medius is available for use under the Apache-2.0 license. For those who need even higher performance, the full-size 70B SuperNova model can be accessed via an Arcee-hosted API or for local deployment. To learn more or explore deployment options, please reach out to [sales@arcee.ai](mailto:sales@arcee.ai).
+
+## Technical Specifications
+
+- **Model Architecture**: Qwen2.5-14B-Instruct
+- **Distillation Sources**: Qwen2.5-72B-Instruct, Llama-3.1-405B-Instruct
+- **Parameter Count**: 14 billion
+- **Training Dataset**: Custom instruction dataset generated with [EvolKit](https://github.com/arcee-ai/EvolKit)
+- **Distillation Technique**: Multi-architecture offline logit distillation with cross-architecture vocabulary alignment.
+
+## Summary
+
+Arcee-SuperNova-Medius provides a unique balance of power, efficiency, and versatility. By distilling knowledge from two top-performing teacher models into a single 14B parameter model, SuperNova-Medius achieves results that rival larger models while maintaining a compact size ideal for practical deployment. Whether for customer support, content creation, or technical assistance, SuperNova-Medius is the perfect choice for organizations looking to leverage advanced language model capabilities in a cost-effective and accessible form.
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_arcee-ai__SuperNova-Medius)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |37.22|
+|IFEval (0-Shot)    |55.60|
+|BBH (3-Shot)       |49.30|
+|MATH Lvl 5 (4-Shot)|32.48|
+|GPQA (0-shot)      |17.90|
+|MuSR (0-shot)      |19.19|
+|MMLU-PRO (5-shot)  |48.83|
+
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task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: IFEval (0-Shot)\n      type: HuggingFaceH4/ifeval\n      args:\n        num_few_shot: 0\n    metrics:\n    - type: inst_level_strict_acc and prompt_level_strict_acc\n      value: 55.6\n      name: strict accuracy\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: BBH (3-Shot)\n      type: BBH\n      args:\n        num_few_shot: 3\n    metrics:\n    - type: acc_norm\n      value: 49.3\n      name: normalized accuracy\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: MATH Lvl 5 (4-Shot)\n      type: hendrycks/competition_math\n      args:\n        num_few_shot: 4\n    metrics:\n    - 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Act as a helpful assistant.' }}\n    {%- endif %}\n    {{- \""\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\"" }}\n    {%- for tool in tools %}\n        {{- \""\\n\"" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \""\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\""name\\\"": <function-name>, \\\""arguments\\\"": <args-json-object>}\\n</tool_call><|im_end|>\\n\"" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are SuperNova-Lite, an intelligent agent created by Arcee AI. Act as a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \""user\"") or (message.role == \""system\"" and not loop.first) or (message.role == \""assistant\"" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \""assistant\"" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\""name\"": \""' }}\n            {{- tool_call.name }}\n            {{- '\"", \""arguments\"": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \""tool\"" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \""tool\"") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \""tool\"") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<|endoftext|>"", ""unk_token"": null}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""eduagarcia/open_pt_llm_leaderboard"", ""logikon/open_cot_leaderboard"", ""cot-leaderboard/open-cot-dashboard"", ""vortex123/SuperNova-Medius"", ""fudyadev/arcee-ai-SuperNova-Medius"", ""LLAI2004/arcee-ai-SuperNova-Medius"", ""H4RDC0123/arcee-ai-SuperNova-Medius"", ""freecad1211/arcee-ai-SuperNova-Medius"", ""atlas0461854/arcee-ai-SuperNova-Medius""], ""safetensors"": {""parameters"": {""BF16"": 14770033664}, ""total"": 14770033664}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-10-28 19:24:03+00:00"", ""cardData"": ""base_model:\n- Qwen/Qwen2.5-14B\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- mergekit\n- merge\nmodel-index:\n- name: SuperNova-Medius\n  results:\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: IFEval (0-Shot)\n      type: HuggingFaceH4/ifeval\n      args:\n        num_few_shot: 0\n    metrics:\n    - type: inst_level_strict_acc and prompt_level_strict_acc\n      value: 55.6\n      name: strict accuracy\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: BBH (3-Shot)\n      type: BBH\n      args:\n        num_few_shot: 3\n    metrics:\n    - type: acc_norm\n      value: 49.3\n      name: normalized accuracy\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: MATH Lvl 5 (4-Shot)\n      type: hendrycks/competition_math\n      args:\n        num_few_shot: 4\n    metrics:\n    - type: exact_match\n      value: 32.48\n      name: exact match\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: GPQA (0-shot)\n      type: Idavidrein/gpqa\n      args:\n        num_few_shot: 0\n    metrics:\n    - type: acc_norm\n      value: 17.9\n      name: acc_norm\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: MuSR (0-shot)\n      type: TAUR-Lab/MuSR\n      args:\n        num_few_shot: 0\n    metrics:\n    - type: acc_norm\n      value: 19.19\n      name: acc_norm\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard\n  - task:\n      type: text-generation\n      name: Text Generation\n    dataset:\n      name: MMLU-PRO (5-shot)\n      type: TIGER-Lab/MMLU-Pro\n      config: main\n      split: test\n      args:\n        num_few_shot: 5\n    metrics:\n    - type: acc\n      value: 48.83\n      name: accuracy\n      verified: false\n    source:\n      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius\n      name: Open LLM Leaderboard"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""66fced1963002e1de073c9b9"", ""modelId"": ""arcee-ai/SuperNova-Medius"", ""usedStorage"": 29558587445}",0,"https://huggingface.co/allura-org/TQ2.5-14B-Neon-v1, https://huggingface.co/mlx-community/SuperNova-Medius-bf16, https://huggingface.co/allura-org/TQ2.5-14B-Sugarquill-v1, https://huggingface.co/Ttimofeyka/Tissint-14B-128k-RP, https://huggingface.co/lucyknada/allura-org_TQ2.5-14B-Sugarquill-v1-exl2",5,https://huggingface.co/ToastyPigeon/supernova-medius-adventure-s-qlora,1,"https://huggingface.co/arcee-ai/SuperNova-Medius-GGUF, https://huggingface.co/bartowski/SuperNova-Medius-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q4_K_S-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q4_K_M-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q5_K_S-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q5_K_M-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q6_K-GGUF, https://huggingface.co/Triangle104/SuperNova-Medius-Q8_0-GGUF, https://huggingface.co/mradermacher/SuperNova-Medius-GGUF, https://huggingface.co/noneUsername/SuperNova-Medius-W8A8-Dynamic-Per-Token, https://huggingface.co/mradermacher/SuperNova-Medius-i1-GGUF, https://huggingface.co/DevQuasar/arcee-ai.SuperNova-Medius-GGUF, https://huggingface.co/mlx-community/SuperNova-Medius-8bit, https://huggingface.co/mlx-community/SuperNova-Medius-4bit, https://huggingface.co/Jianping746/SuperNova-Medius-Q5_K_M-GGUF, https://huggingface.co/tensorblock/SuperNova-Medius-GGUF, https://huggingface.co/nfunctor/SuperNova-Medius-FP8-Dynamic",17,"https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke, https://huggingface.co/Sakalti/Saba2-14B-Preview, https://huggingface.co/djuna/Q2.5-Veltha-14B, https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5, https://huggingface.co/RDson/WomboCombo-R1-Coder-14B-Preview, https://huggingface.co/nlpguy/Lion-Lamarck-v.1.0.8, https://huggingface.co/nlpguy/Lion-Lamarck-v.1.0.9, https://huggingface.co/nlpguy/Lion-Lamarck-v.1.1.0, https://huggingface.co/RDson/CoderO1-14B-Preview, https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-V4-p3, https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-V4, https://huggingface.co/YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4, https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-V5, https://huggingface.co/spacematt/Qwen2.5-Channel-Coder-14B-Instruct, https://huggingface.co/LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1015, https://huggingface.co/LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1016, https://huggingface.co/QuantFactory/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF, https://huggingface.co/mav23/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF, https://huggingface.co/win10/EVA-Meissa-mini-pro, https://huggingface.co/SzilviaB/SuperNova-Qwen-14B, https://huggingface.co/SzilviaB/Qwen-Supernova-14B, https://huggingface.co/QuantFactory/Q2.5-Veltha-14B-GGUF, https://huggingface.co/QuantFactory/Q2.5-Veltha-14B-0.5-GGUF, https://huggingface.co/mergekit-community/Qwen2.5-14B-Merge, https://huggingface.co/CeeJay79/QC_SN-M_14B, https://huggingface.co/mergekit-community/mergekit-sce-nqekivp, https://huggingface.co/attashe/q-2.5-deepseek-r1-veltha-v0.4, https://huggingface.co/RDson/CoderO1-14B-Preview-v2, https://huggingface.co/mergekit-community/Qwen2.5-14B-della-Nova-dpo, https://huggingface.co/pratiknarola/ZYH-LLM-Qwen2.5-14B-V4-GGUF, https://huggingface.co/hardlyworking/MediusMerge",31,"H4RDC0123/arcee-ai-SuperNova-Medius, LLAI2004/arcee-ai-SuperNova-Medius, atlas0461854/arcee-ai-SuperNova-Medius, cot-leaderboard/open-cot-dashboard, eduagarcia/open_pt_llm_leaderboard, freecad1211/arcee-ai-SuperNova-Medius, fudyadev/arcee-ai-SuperNova-Medius, huggingface/InferenceSupport/discussions/new?title=arcee-ai/SuperNova-Medius&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Barcee-ai%2FSuperNova-Medius%5D(%2Farcee-ai%2FSuperNova-Medius)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, logikon/open_cot_leaderboard, open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/SuperNova-Medius, vortex123/SuperNova-Medius",11
+https://huggingface.co/allura-org/TQ2.5-14B-Neon-v1,N/A,N/A,1,,0,,0,,0,,0,,0
+mlx-community/SuperNova-Medius-bf16,"---
+base_model: arcee-ai/SuperNova-Medius
+library_name: transformers
+license: apache-2.0
+tags:
+- mergekit
+- merge
+- mlx
+---
+
+# mlx-community/SuperNova-Medius-bf16
+
+The Model [mlx-community/SuperNova-Medius-bf16](https://huggingface.co/mlx-community/SuperNova-Medius-bf16) was converted to MLX format from [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius) using mlx-lm version **0.19.0**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load(""mlx-community/SuperNova-Medius-bf16"")
+
+prompt=""hello""
+
+if hasattr(tokenizer, ""apply_chat_template"") and tokenizer.chat_template is not None:
+    messages = [{""role"": ""user"", ""content"": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```
+","{""id"": ""mlx-community/SuperNova-Medius-bf16"", ""author"": ""mlx-community"", ""sha"": ""8965f40a6a8c6f982446c0df64fea18242b04b4d"", ""last_modified"": ""2024-10-13 00:17:03+00:00"", ""created_at"": ""2024-10-11 21:32:19+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 4, ""downloads_all_time"": null, ""likes"": 2, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""qwen2"", ""text-generation"", ""mergekit"", ""merge"", ""mlx"", ""conversational"", ""base_model:arcee-ai/SuperNova-Medius"", ""base_model:finetune:arcee-ai/SuperNova-Medius"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: arcee-ai/SuperNova-Medius\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- mergekit\n- merge\n- mlx"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""Qwen2ForCausalLM""], ""model_type"": ""qwen2"", ""tokenizer_config"": {""bos_token"": null, ""chat_template"": ""{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are SuperNova-Lite, an intelligent agent created by Arcee AI. Act as a helpful assistant.' }}\n    {%- endif %}\n    {{- \""\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\"" }}\n    {%- for tool in tools %}\n        {{- \""\\n\"" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \""\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\""name\\\"": <function-name>, \\\""arguments\\\"": <args-json-object>}\\n</tool_call><|im_end|>\\n\"" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are SuperNova-Lite, an intelligent agent created by Arcee AI. Act as a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \""user\"") or (message.role == \""system\"" and not loop.first) or (message.role == \""assistant\"" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \""assistant\"" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\""name\"": \""' }}\n            {{- tool_call.name }}\n            {{- '\"", \""arguments\"": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \""tool\"" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \""tool\"") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \""tool\"") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<|endoftext|>"", ""unk_token"": null}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00006.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""BF16"": 14770033664}, ""total"": 14770033664}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-10-13 00:17:03+00:00"", ""cardData"": ""base_model: arcee-ai/SuperNova-Medius\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- mergekit\n- merge\n- mlx"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""67099963705db29c00c5b687"", ""modelId"": ""mlx-community/SuperNova-Medius-bf16"", ""usedStorage"": 29551555772}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=mlx-community/SuperNova-Medius-bf16&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bmlx-community%2FSuperNova-Medius-bf16%5D(%2Fmlx-community%2FSuperNova-Medius-bf16)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+https://huggingface.co/allura-org/TQ2.5-14B-Sugarquill-v1,N/A,N/A,1,,0,,0,,0,,0,,0
+Ttimofeyka/Tissint-14B-128k-RP,"---
+base_model:
+- arcee-ai/SuperNova-Medius
+library_name: transformers
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- sft
+---
+# Tissint-14B-128k-RP
+---
+![Chat Example](https://i.ibb.co/VqgjW3H/image.png)
+---
+The model is based on [SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius) (as the current best 14B model) with a 128k context with an emphasis on creativity, including NSFW and multi-turn conversations.
+
+According to my tests, this finetune is much more stable with different samplers than the original model. Censorship and refusals have been reduced.
+
+The model started to follow the system prompt better, and the responses in ChatML format with bad samplers stopped reaching 800+ tokens for no reason.
+### Chat Template - ChatML
+## Samplers
+
+### Balance
+```
+Temp : 0.8 - 1.15
+Min P : 0.1
+
+Repetition Penalty : 1.02
+
+DRY 0.8, 1.75, 2, 2048 (change to 4096 or more if needed)
+```
+
+### Creativity
+```
+Temp : 1.15 - 1.5
+Top P : 0.9
+
+Repetition Penalty : 1.03
+
+DRY 0.82, 1.75, 2, 2048 (change to 4096 or more if needed)
+```","{""id"": ""Ttimofeyka/Tissint-14B-128k-RP"", ""author"": ""Ttimofeyka"", ""sha"": ""b7ea476bea786a87ec67ed887bf914340052142b"", ""last_modified"": ""2024-11-10 19:04:21+00:00"", ""created_at"": ""2024-11-09 16:36:09+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1, ""downloads_all_time"": null, ""likes"": 5, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""qwen2"", ""text-generation"", ""unsloth"", ""trl"", ""sft"", ""conversational"", ""base_model:arcee-ai/SuperNova-Medius"", ""base_model:finetune:arcee-ai/SuperNova-Medius"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- arcee-ai/SuperNova-Medius\nlibrary_name: transformers\nlicense: 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'<|im_end|>\n'}}{% elif message['from'] == 'gpt' %}{{'<|im_start|>assistant\n' + message['value'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['value'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<|endoftext|>"", ""unk_token"": null}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='added_tokens.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00001-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00002-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00003-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00004-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00005-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model-00006-of-00006.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='pytorch_model.bin.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-11-10 19:04:21+00:00"", ""cardData"": ""base_model:\n- arcee-ai/SuperNova-Medius\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- unsloth\n- trl\n- sft"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""672f8f7948b31810a2690d52"", ""modelId"": ""Ttimofeyka/Tissint-14B-128k-RP"", ""usedStorage"": 59091823738}",1,"https://huggingface.co/Ttimofeyka/Tissint-14B-v1.2-128k-RP, https://huggingface.co/Ttimofeyka/Tissint-14B-v1.1-128k-RP",2,,0,"https://huggingface.co/mradermacher/Tissint-14B-128k-RP-GGUF, https://huggingface.co/mradermacher/Tissint-14B-128k-RP-i1-GGUF",2,,0,huggingface/InferenceSupport/discussions/new?title=Ttimofeyka/Tissint-14B-128k-RP&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BTtimofeyka%2FTissint-14B-128k-RP%5D(%2FTtimofeyka%2FTissint-14B-128k-RP)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+https://huggingface.co/Ttimofeyka/Tissint-14B-v1.2-128k-RP,N/A,N/A,2,,0,,0,,0,,0,,0
+https://huggingface.co/Ttimofeyka/Tissint-14B-v1.1-128k-RP,N/A,N/A,2,,0,,0,,0,,0,,0
+https://huggingface.co/lucyknada/allura-org_TQ2.5-14B-Sugarquill-v1-exl2,N/A,N/A,1,,0,,0,,0,,0,,0

Van-Gogh-diffusion_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,67 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+dallinmackay/Van-Gogh-diffusion,"---
+license: creativeml-openrail-m
+thumbnail: ""https://huggingface.co/dallinmackay/Van-Gogh-diffusion/resolve/main/preview1.jpg""
+tags:
+- stable-diffusion
+- text-to-image
+---
+### Van Gogh Diffusion
+
+v2 - fixed and working
+
+This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from the film **_Loving Vincent_**. Use the token **_lvngvncnt_** at the BEGINNING of your prompts to use the style (e.g., ""lvngvncnt, beautiful woman at sunset""). This model works best with the Euler sampler (NOT Euler_a).
+
+_Download the ckpt file from ""files and versions"" tab into the stable diffusion models folder of your web-ui of choice._
+
+If you get too many yellow faces or you dont like the strong blue bias, simply put them in the negative prompt (e.g., ""Yellow face, blue"").
+
+--
+
+**Characters rendered with this model:**
+![Character Samples](https://huggingface.co/dallinmackay/Van-Gogh-diffusion/resolve/main/preview1.jpg)
+  _prompt and settings used: **lvngvncnt, [person], highly detailed** | **Steps: 25, Sampler: Euler, CFG scale: 6**_
+
+--
+
+**Landscapes/miscellaneous rendered with this model:**
+![Landscape Samples](https://huggingface.co/dallinmackay/Van-Gogh-diffusion/resolve/main/preview2.jpg)
+  _prompt and settings used: **lvngvncnt, [subject/setting], highly detailed** | **Steps: 25, Sampler: Euler, CFG scale: 6**_
+
+--
+
+This model was trained with Dreambooth, using TheLastBen colab notebook
+--
+### 🧨 Diffusers
+
+This model can be used just like any other Stable Diffusion model. For more information,
+please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
+
+You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = ""dallinmackay/Van-Gogh-diffusion""
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to(""cuda"")
+
+prompt = ""lvngvncnt, beautiful woman at sunset""
+image = pipe(prompt).images[0]
+
+image.save(""./sunset.png"")
+```
+
+## License
+
+This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+The CreativeML OpenRAIL License specifies: 
+
+1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 
+2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
+
+--
+[![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://www.patreon.com/dallinmackay)","{""id"": ""dallinmackay/Van-Gogh-diffusion"", ""author"": ""dallinmackay"", ""sha"": ""b02f2d853812d352e9d9023c2aae091878f1ee1e"", ""last_modified"": ""2023-05-16 09:25:20+00:00"", ""created_at"": ""2022-11-05 00:26:09+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 769, ""downloads_all_time"": null, ""likes"": 282, ""library_name"": ""diffusers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""diffusers"", ""stable-diffusion"", ""text-to-image"", ""license:creativeml-openrail-m"", ""autotrain_compatible"", ""endpoints_compatible"", ""diffusers:StableDiffusionPipeline"", ""region:us""], ""pipeline_tag"": ""text-to-image"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: creativeml-openrail-m\ntags:\n- stable-diffusion\n- text-to-image\nthumbnail: 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""RepoSibling(rfilename='safety_checker/pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='scheduler/scheduler_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='text_encoder/pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer/vocab.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='unet/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='unet/diffusion_pytorch_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vae/diffusion_pytorch_model.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [""anzorq/finetuned_diffusion"", ""Yntec/ToyWorld"", ""Yntec/PrintingPress"", ""Nymbo/image_gen_supaqueue"", ""ennov8ion/3dart-Models"", ""phenixrhyder/NSFW-ToyWorld"", ""Yntec/blitz_diffusion"", ""sanaweb/text-to-image"", ""BilalSardar/Text-To-image-AllModels"", ""AdamOswald1/finetuned_diffusion"", ""Vedits/6x_Image_diffusion"", ""John6666/Diffusion80XX4sg"", ""ennov8ion/comicbook-models"", ""John6666/PrintingPress4"", ""Nickhilearla135095/maximum_diffusion"", ""SUPERSHANKY/Finetuned_Diffusion_Max"", ""PeepDaSlan9/B2BMGMT_Diffusion60XX"", ""Joeythemonster/Text-To-image-AllModels"", ""Evel/Evel_Space"", ""Daniela-C/6x_Image_diffusion"", ""Dao3/Text-To-image-AllModels"", ""phenixrhyder/PrintingPress"", ""John6666/hfd_test_nostopbutton"", ""mindtube/Diffusion50XX"", ""Nymbo/Diffusion80XX4sg"", ""kaleidoskop-hug/PrintingPress"", ""ennov8ion/stablediffusion-models"", ""ReiPlush64/finetuned_diffusion"", ""John6666/ToyWorld4"", ""Omnibus-archive/Diffusion-Flood"", ""grzegorz2047/fast_diffusion"", ""Alfasign/dIFFU"", ""Nymbo/PrintingPress"", ""Rifd/Sdallmodels"", ""John6666/Diffusion80XX4g"", ""NativeAngels/HuggingfaceDiffusion"", ""ennov8ion/Scifi-Models"", ""ennov8ion/semirealistic-models"", ""Jackflack09/finetuned_diffusion2"", ""ennov8ion/dreamlike-models"", ""ennov8ion/FantasyArt-Models"", ""noes14155/img_All_models"", ""AnimeStudio/anime-models"", ""John6666/Diffusion80XX4"", ""K00B404/HuggingfaceDiffusion_custom"", ""John6666/blitz_diffusion4"", ""John6666/blitz_diffusion_builtin"", ""deaf1296/finetuned_diffusion"", ""Lyra121/finetuned_diffusion"", ""hylee/finetuned_diffusion"", ""Rooni/finetuned_diffusion"", ""RhythmRemix14/PrintingPressDx"", ""sohoso/PrintingPress"", ""NativeAngels/ToyWorld"", ""gusvd/dallinmackay-Van-Gogh-diffusion"", ""Harshveer/Finetuned_Diffusion_Max"", ""gato001k1/maximum_diffusion0k"", ""mindtube/maximum_multiplier_places"", ""animeartstudio/ArtModels"", ""Binettebob22/fast_diffusion2"", ""pikto/Elite-Scifi-Models"", ""PixelistStudio/3dart-Models"", ""devmiles/zexxiai"", ""Nymbo/Diffusion60XX"", ""TheKitten/Images"", ""ennov8ion/anime-models"", ""jordonpeter01/Diffusion70"", ""Joeythemonster/finetuned_diffusion"", ""kazumak/sdspace"", ""prikmmo9/finetuned_diffusion"", ""georgesX/finetuned_diffusion"", ""kerria/finetuned_diffusion"", ""ygtrfed/pp-web-ui"", ""ivanmeyer/Finetuned_Diffusion_Max"", ""ennov8ion/Landscapes-models"", ""ucmisanddisinfo/thisApp"", ""johann22/chat-diffusion"", ""Omnibus/2-button-Story-Board"", ""manivannan7gp/Words2Image"", ""ennov8ion/art-models"", ""ennov8ion/photo-models"", ""ennov8ion/art-multi"", ""NativeAngels/blitz_diffusion"", ""NativeAngels/PrintingPress4"", ""NativeAngels/PrintingPress"", ""dehua68/ToyWorld"", ""burman-ai/Printing-Press"", ""sk16er/ghibli_creator"", ""amanullahmenjli/tifusion"", ""BudakMabuk/finetuned_diffusion"", ""WagnerPPA/finetuned_diffusion"", ""refugelu/finetuned_diffusion"", ""chantysothy/Evel_Space"", ""Frei/finetuned_diffusion"", ""shibili/finetuned_diffusion"", ""Camjo11/Evel_Space"", ""Senpaisora6/Evel_Space"", ""fractalneuron/Evel_Space"", ""emkdb/Evel_Space"", ""slayrrc/finetuned_diffusion""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-05-16 09:25:20+00:00"", ""cardData"": ""license: creativeml-openrail-m\ntags:\n- stable-diffusion\n- text-to-image\nthumbnail: https://huggingface.co/dallinmackay/Van-Gogh-diffusion/resolve/main/preview1.jpg"", ""transformersInfo"": null, ""_id"": ""6365ada125aa3bd177d62244"", ""modelId"": ""dallinmackay/Van-Gogh-diffusion"", ""usedStorage"": 21348820339}",0,,0,,0,,0,,0,"CompVis/stable-diffusion-license, Daniela-C/6x_Image_diffusion, Joeythemonster/Text-To-image-AllModels, John6666/Diffusion80XX4sg, John6666/PrintingPress4, John6666/hfd_test_nostopbutton, Nymbo/image_gen_supaqueue, PeepDaSlan9/B2BMGMT_Diffusion60XX, Yntec/PrintingPress, Yntec/ToyWorld, Yntec/blitz_diffusion, huggingface/InferenceSupport/discussions/new?title=dallinmackay/Van-Gogh-diffusion&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bdallinmackay%2FVan-Gogh-diffusion%5D(%2Fdallinmackay%2FVan-Gogh-diffusion)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kaleidoskop-hug/PrintingPress, phenixrhyder/NSFW-ToyWorld",14

Wayfarer-12B_finetunes_20250426_221535.csv_finetunes_20250426_221535.csv

@@ -0,0 +1,72 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+LatitudeGames/Wayfarer-12B,"---
+license: apache-2.0
+language:
+- en
+base_model:
+- mistralai/Mistral-Nemo-Base-2407
+tags:
+- text adventure
+- roleplay
+library_name: transformers
+---
+
+![image/jpeg](wayfarer.jpg)
+
+# Wayfarer-12B
+We’ve heard over and over from AI Dungeon players that modern AI models are too nice, never letting them fail or die. While it may be good for a chatbot to be nice and helpful, great stories and games aren’t all rainbows and unicorns. They have conflict, tension, and even death. These create real stakes and consequences for characters and the journeys they go on.
+
+Similarly, great games need opposition. You must be able to fail, die, and may even have to start over. This makes games more fun! 
+
+However, the vast majority of AI models, through alignment RLHF, have been trained away from darkness, violence, or conflict, preventing them from fulfilling this role. To give our players better options, we decided to train our own model to fix these issues.
+
+Wayfarer is an adventure role-play model specifically trained to give players a challenging and dangerous experience. We thought they would like it, but since releasing it on AI Dungeon, players have reacted even more positively than we expected.
+
+Because they loved it so much, we’ve decided to open-source the model so anyone can experience unforgivingly brutal AI adventures! Anyone can download the model to run locally.
+
+Or if you want to easily try this model for free, you can do so at [https://aidungeon.com](https://aidungeon.com/).
+
+We plan to continue improving and open-sourcing similar models, so please share any and all feedback on how we can improve model behavior. Below we share more details on how Wayfarer was created.
+
+[Quantized GGUF weights can be downloaded here.](https://huggingface.co/LatitudeGames/Wayfarer-12B-GGUF)
+
+## Model details
+Wayfarer 12B was trained on top of the Nemo base model using a two-stage SFT approach, with the first stage containing 180K chat-formatted instruct data instances and the second stage consisting of a 50/50 mixture of synthetic 8k context text adventures and roleplay experiences.
+
+## How It Was Made
+Wayfarer’s text adventure data was generated by simulating playthroughs of published character creator scenarios from AI Dungeon. Five distinct user archetypes played through each scenario, whose character starts all varied in faction, location, etc. to generate five unique samples.
+
+One language model played the role of narrator, with the other playing the user. They were blind to each other’s underlying logic, so the user was actually capable of surprising the narrator with their choices. Each simulation was allowed to run for 8k tokens or until the main character died.
+
+Wayfarer’s general emotional sentiment is one of pessimism, where failure is frequent and plot armor does not exist. This serves to counter the positivity bias so inherent in our language models nowadays.
+
+## Inference
+The Nemo architecture is known for being sensitive to higher temperatures, so the following settings are recommended as a baseline. Nothing stops you from experimenting with these, of course.
+
+```
+""temperature"": 0.8,
+""repetition_penalty"": 1.05,
+""min_p"": 0.025
+```
+
+## Limitations
+Wayfarer was trained exclusively on second-person present tense data (using “you”) in a narrative style. Other styles will work as well but may produce suboptimal results.
+
+Additionally, Wayfarer was trained exclusively on single-turn chat data.
+
+## Prompt Format
+ChatML was used for both finetuning stages.
+
+```
+<|im_start|>system
+You're a masterful storyteller and gamemaster. Write in second person present tense (You are), crafting vivid, engaging narratives with authority and confidence.<|im_end|>
+<|im_start|>user
+> You peer into the darkness.<|im_end|>
+<|im_start|>assistant
+You have been eaten by a grue.
+
+GAME OVER<|im_end|>
+```
+
+## Credits
+Thanks to [Gryphe Padar](https://huggingface.co/Gryphe) for collaborating on this finetune with us!","{""id"": ""LatitudeGames/Wayfarer-12B"", ""author"": ""LatitudeGames"", ""sha"": ""d447dc49b22394fef3ad9b804037c02634aa8ff3"", ""last_modified"": ""2025-01-17 14:49:47+00:00"", ""created_at"": ""2025-01-03 09:33:16+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 15086, ""downloads_all_time"": null, ""likes"": 193, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""mistral"", ""text-generation"", ""text adventure"", ""roleplay"", ""conversational"", ""en"", ""base_model:mistralai/Mistral-Nemo-Base-2407"", ""base_model:finetune:mistralai/Mistral-Nemo-Base-2407"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- mistralai/Mistral-Nemo-Base-2407\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- text adventure\n- roleplay"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""MistralForCausalLM""], ""model_type"": ""mistral"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""chat_template"": ""{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<pad>"", ""unk_token"": ""<unk>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00005.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='wayfarer.jpg', size=None, blob_id=None, lfs=None)""], ""spaces"": [""KBaba7/Quant"", ""bhaskartripathi/LLM_Quantization"", ""totolook/Quant"", ""FallnAI/Quantize-HF-Models"", ""ruslanmv/convert_to_gguf"", ""bobscorporation/LatitudeGames-Wayfarer-12B"", ""K00B404/LLM_Quantization""], ""safetensors"": {""parameters"": {""BF16"": 12247802880}, ""total"": 12247802880}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-01-17 14:49:47+00:00"", ""cardData"": ""base_model:\n- mistralai/Mistral-Nemo-Base-2407\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\ntags:\n- text adventure\n- roleplay"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""6777aedcbbd0ef497bf5bf8b"", ""modelId"": ""LatitudeGames/Wayfarer-12B"", ""usedStorage"": 24512726732}",0,,0,,0,"https://huggingface.co/LatitudeGames/Wayfarer-12B-GGUF, https://huggingface.co/Triangle104/Wayfarer-12B-Q5_K_S-GGUF, https://huggingface.co/mradermacher/Wayfarer-12B-GGUF, https://huggingface.co/mradermacher/Wayfarer-12B-i1-GGUF, https://huggingface.co/mlx-community/Wayfarer-12B-6bit, https://huggingface.co/mlx-community/Wayfarer-12B-4bit, https://huggingface.co/mlx-community/Wayfarer-12B-8bit, https://huggingface.co/tensorblock/Wayfarer-12B-GGUF, https://huggingface.co/bartowski/Wayfarer-12B-GGUF, https://huggingface.co/Zuellni/Wayfarer-12B-5.0bpw-exl2, https://huggingface.co/Zuellni/Wayfarer-12B-6.0bpw-exl2, https://huggingface.co/Triangle104/Wayfarer-12B-Q8_0-GGUF, https://huggingface.co/Triangle104/Wayfarer-12B-Q4_K_S-GGUF, https://huggingface.co/Triangle104/Wayfarer-12B-Q4_K_M-GGUF, https://huggingface.co/Triangle104/Wayfarer-12B-Q5_K_M-GGUF, https://huggingface.co/Triangle104/Wayfarer-12B-Q6_K-GGUF, https://huggingface.co/Jellon/Wayfarer-12B-exl2-6bpw, https://huggingface.co/DevQuasar/LatitudeGames.Wayfarer-12B-GGUF, https://huggingface.co/Jellon/Wayfarer-12B-exl2-4bpw, https://huggingface.co/mrfakename/Wayfarer-12B-Q4-mlx, https://huggingface.co/noneUsername/Wayfarer-12B-W8A8, https://huggingface.co/waldie/Wayfarer-12B-8bpw-h8-exl2",22,"https://huggingface.co/Lambent/Silver5-Nemo-12B, https://huggingface.co/redrix/wuriaee-12B-schizostock, https://huggingface.co/redrix/sororicide-12B-Farer-Mell-Unslop, https://huggingface.co/redrix/GodSlayer-12B-ABYSS, https://huggingface.co/Nitral-AI/Wayfarer_Eris_Noctis-12B, https://huggingface.co/Aleteian/Pathfinder-RP-12B-RU, https://huggingface.co/KatyTheCutie/Repose-12B, https://huggingface.co/ClaudioItaly/Sensorial-V2-12B, https://huggingface.co/Aleteian/Legend-of-the-Four-Winds-MN-12B, https://huggingface.co/Aleteian/Way-to-Unseen-Horizon-MN-12B, https://huggingface.co/mergekit-community/MN-Sappho-a-12B, https://huggingface.co/mergekit-community/MN-Sappho-b-12B, https://huggingface.co/mergekit-community/MN-Sappho-c-12B, https://huggingface.co/KatyTheCutie/Repose-V2-A2, https://huggingface.co/KatyTheCutie/Repose-V2-6O, https://huggingface.co/Aleteian/Legend-of-the-Four-Winds-2-MN-12B, https://huggingface.co/mergekit-community/MN-Sappho-e-12B, https://huggingface.co/mergekit-community/MN-Sappho-g-12B, https://huggingface.co/mergekit-community/MN-Sappho-g2-12B, https://huggingface.co/Aleteian/Hello-Darkness-My-Old-Friend-MN-12B, https://huggingface.co/mergekit-community/MN-Sappho-l-12B, https://huggingface.co/mergekit-community/MN-Sappho-m-12B, https://huggingface.co/mergekit-community/MN-Sappho-g3-12B, https://huggingface.co/yamatazen/Aurora-SCE-12B, https://huggingface.co/mergekit-community/MN-Sappho-n-12B, https://huggingface.co/mergekit-community/MN-Sappho-n2-12B, https://huggingface.co/SanXM1/Driftwood-12B, https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-Nocturne, https://huggingface.co/mergekit-community/MN-Anathema-12B, https://huggingface.co/mergekit-community/MN-Hecate-Chthonia-12B, https://huggingface.co/mergekit-community/MN-Hekate-Geneteira-12B, https://huggingface.co/Triangle104/Wayfare-Chronicles-Ink_12B, https://huggingface.co/Triangle104/Wayfare-Chronicles-Gold_12B, https://huggingface.co/Triangle104/Wayfare-Chronicles-Gutenberg_12B, https://huggingface.co/mergekit-community/MN-Slush-Wayfarer, https://huggingface.co/IAmTheCollector/MN-Slush-GGLD-Wayfarer, https://huggingface.co/Aleteian/Wayfinder, https://huggingface.co/Aleteian/WayToHumanity, https://huggingface.co/ClaudioItaly/Sensorial-12B, https://huggingface.co/mergekit-community/ChatWaifu-Wayfarer-12B, https://huggingface.co/mergekit-community/mergekit-task_arithmetic-zxjskqt, https://huggingface.co/mergekit-community/mergekit-model_stock-hwudfad, https://huggingface.co/mergekit-community/MN-Sappho-h-12B, https://huggingface.co/mergekit-community/MN-Sappho-i-12B, https://huggingface.co/mergekit-community/MN-Sappho-n3-12B, https://huggingface.co/NewEden/Delta-Vector_driftwood-exl2, https://huggingface.co/mergekit-community/mergekit-ties-okotcvk, https://huggingface.co/mergekit-community/mergekit-dare_ties-eovqfju, https://huggingface.co/mergekit-community/MN-Hekate-Nykhia-17B, https://huggingface.co/DreadPoor/YM-12B-Model_Stock, https://huggingface.co/mergekit-community/MN-Hekate-Noctiluca-12B, https://huggingface.co/mergekit-community/MN-Hekate-Noctiluca-12B-v2",52,"FallnAI/Quantize-HF-Models, K00B404/LLM_Quantization, KBaba7/Quant, bhaskartripathi/LLM_Quantization, bobscorporation/LatitudeGames-Wayfarer-12B, huggingface/InferenceSupport/discussions/new?title=LatitudeGames/Wayfarer-12B&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BLatitudeGames%2FWayfarer-12B%5D(%2FLatitudeGames%2FWayfarer-12B)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, ruslanmv/convert_to_gguf, totolook/Quant",8

XTTS-v1_finetunes_20250426_014322.csv_finetunes_20250426_014322.csv

@@ -0,0 +1,93 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+coqui/XTTS-v1,"---
+license: other
+license_name: coqui-public-model-license
+license_link: https://coqui.ai/cpml
+library_name: coqui
+pipeline_tag: text-to-speech
+---
+
+# ⓍTTS
+ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. Built on Tortoise,
+ⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy.
+There is no need for an excessive amount of training data that spans countless hours.
+
+This is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui API](https://docs.coqui.ai/docs), however we apply
+a few tricks to make it faster and support streaming inference.
+
+## NOTE: ⓍTTS V2 model is out here [XTTS V2](https://huggingface.co/coqui/XTTS-v2)
+
+### Features
+- Supports 14 languages. 
+- Voice cloning with just a 6-second audio clip.
+- Emotion and style transfer by cloning. 
+- Cross-language voice cloning.
+- Multi-lingual speech generation.
+- 24khz sampling rate.
+
+### Languages
+As of now, XTTS-v1 (v1.1) supports 14 languages: **English, Spanish, French, German, Italian, Portuguese,
+Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese, and Japanese**.
+
+Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out!
+
+### Code
+The current implementation supports inference and [fine-tuning](https://tts.readthedocs.io/en/latest/models/xtts.html#training).
+
+### License
+This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml).
+
+### Contact
+Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai).
+You can also mail us at info@coqui.ai.
+
+Using 🐸TTS API:
+
+```python
+from TTS.api import TTS
+tts = TTS(""tts_models/multilingual/multi-dataset/xtts_v1"", gpu=True)
+
+# generate speech by cloning a voice using default settings
+tts.tts_to_file(text=""It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."",
+                file_path=""output.wav"",
+                speaker_wav=""/path/to/target/speaker.wav"",
+                language=""en"")
+
+# generate speech by cloning a voice using custom settings
+tts.tts_to_file(text=""It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."",
+                file_path=""output.wav"",
+                speaker_wav=""/path/to/target/speaker.wav"",
+                language=""en"",
+                decoder_iterations=30)
+```
+
+Using 🐸TTS Command line:
+
+```console
+ tts --model_name tts_models/multilingual/multi-dataset/xtts_v1 \
+     --text ""Bugün okula gitmek istemiyorum."" \
+     --speaker_wav /path/to/target/speaker.wav \
+     --language_idx tr \
+     --use_cuda true
+```
+
+Using model directly:
+
+```python
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+config = XttsConfig()
+config.load_json(""/path/to/xtts/config.json"")
+model = Xtts.init_from_config(config)
+model.load_checkpoint(config, checkpoint_dir=""/path/to/xtts/"", eval=True)
+model.cuda()
+
+outputs = model.synthesize(
+    ""It took me quite a long time to develop a voice and now that I have it I am not going to be silent."",
+    config,
+    speaker_wav=""/data/TTS-public/_refclips/3.wav"",
+    gpt_cond_len=3,
+    language=""en"",
+)
+```","{""id"": ""coqui/XTTS-v1"", ""author"": ""coqui"", ""sha"": ""590756d186823eb1e9141be223b00a8ebc998c54"", ""last_modified"": ""2023-11-10 19:40:20+00:00"", ""created_at"": ""2023-09-13 09:22:03+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 5684, ""downloads_all_time"": null, ""likes"": 369, ""library_name"": ""coqui"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""coqui"", ""text-to-speech"", ""license:other"", ""region:us""], ""pipeline_tag"": ""text-to-speech"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""library_name: coqui\nlicense: other\nlicense_name: coqui-public-model-license\nlicense_link: https://coqui.ai/cpml\npipeline_tag: text-to-speech"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config_v1.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.pth', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [""Olivier-Truong/XTTS_V1_CPU_working"", ""Olivier-Truong/XTTS_V2_CPU_working"", ""kotoba-tech/TTS-Arena-JA"", ""mrm8488/xtts-spanish"", ""Egmdon/XTTS_V2_CPU_working"", ""gorkemgoknar/xtts-streaming"", ""LPhilp1943/speech2speech_voice_cloning_v2"", ""Fabrice-TIERCELIN/Multi-language_Text-to-Speech"", ""antoniomae/xtts2"", ""ysharma/xtts"", ""kevinwang676/xtts"", ""lucianne/XTTS_V3_CPU_working"", ""Maoweicao/xttsv2"", ""1littlecoder/xtts"", ""gukisan/xtts"", ""espeon/xtts"", ""khetag/xtts"", ""Zannriell/xtts"", ""valentinlica/xtts"", ""sand-box/nnf_text_to_speech_v2_dev"", ""vivasvan100/xtts"", ""reach-vb/xtts-streaming"", ""antoniomae/coquixtts"", ""nitintit/xtts"", ""cocktailpeanut/xtts-streaming"", ""antoniomae1234/HTTS-VOITS-XTTS"", ""com-ple7e/XTTS-running-on-basic-cpu"", ""krishna195/Krishna_TTS_model"", ""vuxuanhoan/XTTS_V1_CPU_working"", ""gregory-237/ATLANTA_TSS"", ""Desident/Multi-language_Text-to-Speech""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-11-10 19:40:20+00:00"", ""cardData"": ""library_name: coqui\nlicense: other\nlicense_name: coqui-public-model-license\nlicense_link: https://coqui.ai/cpml\npipeline_tag: text-to-speech"", ""transformersInfo"": null, ""_id"": ""65017f3bf0e75a40c4115cb0"", ""modelId"": ""coqui/XTTS-v1"", ""usedStorage"": 33359253546}",0,,0,,0,,0,,0,"Egmdon/XTTS_V2_CPU_working, Fabrice-TIERCELIN/Multi-language_Text-to-Speech, LPhilp1943/speech2speech_voice_cloning_v2, Olivier-Truong/XTTS_V1_CPU_working, Olivier-Truong/XTTS_V2_CPU_working, antoniomae/xtts2, gorkemgoknar/xtts-streaming, huggingface/InferenceSupport/discussions/new?title=coqui/XTTS-v1&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bcoqui%2FXTTS-v1%5D(%2Fcoqui%2FXTTS-v1)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kevinwang676/xtts, kotoba-tech/TTS-Arena-JA, lucianne/XTTS_V3_CPU_working, mrm8488/xtts-spanish, ysharma/xtts",13

canary-1b_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,546 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+nvidia/canary-1b,"---
+license: cc-by-nc-4.0
+language:
+- en
+- de
+- es
+- fr
+library_name: nemo
+datasets:
+- librispeech_asr
+- fisher_corpus
+- Switchboard-1
+- WSJ-0
+- WSJ-1
+- National-Singapore-Corpus-Part-1
+- National-Singapore-Corpus-Part-6
+- vctk
+- voxpopuli
+- europarl
+- multilingual_librispeech
+- mozilla-foundation/common_voice_8_0
+- MLCommons/peoples_speech
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- automatic-speech-translation
+- speech
+- audio
+- Transformer
+- FastConformer
+- Conformer
+- pytorch
+- NeMo
+- hf-asr-leaderboard
+widget:
+- example_title: Librispeech sample 1
+  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+- example_title: Librispeech sample 2
+  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+model-index:
+- name: canary-1b
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: LibriSpeech (other)
+      type: librispeech_asr
+      config: other
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 2.89
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: SPGI Speech
+      type: kensho/spgispeech
+      config: test
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 4.79
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: en
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER (En)
+      type: wer
+      value: 7.97
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: de
+      split: test
+      args:
+        language: de
+    metrics:
+    - name: Test WER (De)
+      type: wer
+      value: 4.61
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: es
+      split: test
+      args:
+        language: es
+    metrics:
+    - name: Test WER (ES)
+      type: wer
+      value: 3.99
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: fr
+      split: test
+      args:
+        language: fr
+    metrics:
+    - name: Test WER (Fr)
+      type: wer
+      value: 6.53
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->De)
+      type: bleu
+      value: 32.15
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->Es)
+      type: bleu
+      value: 22.66
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->Fr)
+      type: bleu
+      value: 40.76
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: de_de
+      split: test
+      args:
+        language: de-en
+    metrics:
+    - name: Test BLEU (De->En)
+      type: bleu
+      value: 33.98
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: es_419
+      split: test
+      args:
+        language: es-en
+    metrics:
+    - name: Test BLEU (Es->En)
+      type: bleu
+      value: 21.80
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: fr_fr
+      split: test
+      args:
+        language: fr-en
+    metrics:
+    - name: Test BLEU (Fr->En)
+      type: bleu
+      value: 30.95
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: de_de
+      split: test
+      args:
+        language: de-en
+    metrics:
+    - name: Test BLEU (De->En)
+      type: bleu
+      value: 37.67
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: es_419
+      split: test
+      args:
+        language: es-en
+    metrics:
+    - name: Test BLEU (Es->En)
+      type: bleu
+      value: 40.7
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: fr_fr
+      split: test
+      args:
+        language: fr-en
+    metrics:
+    - name: Test BLEU (Fr->En)
+      type: bleu
+      value: 40.42
+  
+metrics:
+- wer
+- bleu
+pipeline_tag: automatic-speech-recognition
+---
+
+
+# Canary 1B
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--Transformer-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-1B-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-multilingual-lightgrey#model-badge)](#datasets)
+
+NVIDIA [NeMo Canary](https://nvidia.github.io/NeMo/blogs/2024/2024-02-canary/) is a family of multi-lingual multi-tasking models that achieves state-of-the art performance on multiple benchmarks. With 1 billion parameters, Canary-1B supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). 
+
+**🚨Note: Checkout our latest [Canary-1B-Flash](https://huggingface.co/nvidia/canary-1b-flash) model, a faster and more accurate variant of Canary-1B!**
+
+## Model Architecture
+
+Canary is an encoder-decoder model with FastConformer [1] encoder and Transformer Decoder [2]. 
+With audio features extracted from the encoder, task tokens such as `<source language>`, `<target language>`, `<task>` and `<toggle PnC>` 
+are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual 
+SentencePiece [3] tokenizers of each language, which makes it easy to scale up to more languages. 
+The Canay-1B model has 24 encoder layers and 24 layers of decoder layers in total.
+
+
+## NVIDIA NeMo
+
+To train, fine-tune or Transcribe with Canary, you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed Cython and latest PyTorch version.
+```
+pip install git+https://github.com/NVIDIA/NeMo.git@r1.23.0#egg=nemo_toolkit[asr]
+```
+
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Loading the Model
+
+```python
+from nemo.collections.asr.models import EncDecMultiTaskModel
+
+# load model
+canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
+
+# update dcode params
+decode_cfg = canary_model.cfg.decoding
+decode_cfg.beam.beam_size = 1
+canary_model.change_decoding_strategy(decode_cfg)
+```
+
+### Input Format
+Input to Canary can be either a list of paths to audio files or a jsonl manifest file.
+
+If the input is a list of paths, Canary assumes that the audio is English and Transcribes it. I.e., Canary default behaviour is English ASR. 
+```python
+predicted_text = canary_model.transcribe(
+    paths2audio_files=['path1.wav', 'path2.wav'],
+    batch_size=16,  # batch size to run the inference with
+)[0].text
+```
+
+To use Canary for transcribing other supported languages or perform Speech-to-Text translation, specify the input as jsonl manifest file, where each line in the file is a dictionary containing the following fields: 
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    ""audio_filepath"": ""/path/to/audio.wav"",  # path to the audio file
+    ""duration"": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    ""taskname"": ""asr"",  # use ""s2t_translation"" for speech-to-text translation with r1.23, or ""ast"" if using the NeMo main branch
+    ""source_lang"": ""en"",  # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
+    ""target_lang"": ""en"",  # language of the text output, choices=['en','de','es','fr']
+    ""pnc"": ""yes"",  # whether to have PnC output, choices=['yes', 'no']
+    ""answer"": ""na"", 
+}
+```
+
+and then use:
+```python
+predicted_text = canary_model.transcribe(
+    ""<path to input manifest file>"",
+    batch_size=16,  # batch size to run the inference with
+)[0].text
+```
+
+
+### Automatic Speech-to-text Recognition (ASR)
+
+An example manifest for transcribing English audios can be:
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    ""audio_filepath"": ""/path/to/audio.wav"",  # path to the audio file
+    ""duration"": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    ""taskname"": ""asr"",  
+    ""source_lang"": ""en"", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
+    ""target_lang"": ""en"", # language of the text output, choices=['en','de','es','fr']
+    ""pnc"": ""yes"",  # whether to have PnC output, choices=['yes', 'no']
+    ""answer"": ""na"", 
+}
+```
+
+
+### Automatic Speech-to-text Translation (AST)
+
+An example manifest for transcribing English audios into German text can be:
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    ""audio_filepath"": ""/path/to/audio.wav"",  # path to the audio file
+    ""duration"": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    ""taskname"": ""s2t_translation"", # r1.23 only recognizes ""s2t_translation"", but ""ast"" is supported if using the NeMo main branch
+    ""source_lang"": ""en"", # language of the audio input, choices=['en','de','es','fr']
+    ""target_lang"": ""de"", # language of the text output, choices=['en','de','es','fr']
+    ""pnc"": ""yes"",  # whether to have PnC output, choices=['yes', 'no']
+    ""answer"": ""na"" 
+}
+```
+
+Alternatively, one can use `transcribe_speech.py` script to do the same. 
+
+```bash
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
+ pretrained_name=""nvidia/canary-1b"" 
+ audio_dir=""<path to audio_directory>"" # transcribes all the wav files in audio_directory
+```
+
+
+```bash
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
+ pretrained_name=""nvidia/canary-1b"" 
+ dataset_manifest=""<path to manifest file>"" 
+```
+
+
+### Input
+
+This model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.
+
+### Output
+
+The model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.
+
+
+
+## Training
+
+Canary-1B is trained using the  NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs. 
+The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_multitask/speech_to_text_aed.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml).
+
+The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
+
+
+### Datasets
+
+The Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by [Suno](https://suno.ai/), and 34k hrs of in-house data. 
+
+The constituents of public data are as follows. 
+
+#### English (25.5k hours)
+- Librispeech 960 hours
+- Fisher Corpus
+- Switchboard-1 Dataset
+- WSJ-0 and WSJ-1
+- National Speech Corpus (Part 1, Part 6)
+- VCTK
+- VoxPopuli (EN)
+- Europarl-ASR (EN)
+- Multilingual Librispeech (MLS EN) - 2,000 hour subset
+- Mozilla Common Voice (v7.0)
+- People's Speech - 12,000 hour subset
+- Mozilla Common Voice (v11.0)  - 1,474 hour subset
+
+#### German (2.5k hours)
+- Mozilla Common Voice (v12.0)  - 800 hour subset
+- Multilingual Librispeech (MLS DE) - 1,500 hour subset
+- VoxPopuli (DE) - 200 hr subset
+
+#### Spanish (1.4k hours)
+- Mozilla Common Voice (v12.0)  - 395 hour subset
+- Multilingual Librispeech (MLS ES) - 780 hour subset
+- VoxPopuli (ES) - 108 hour subset
+- Fisher  - 141 hour subset
+
+#### French (1.8k hours)
+- Mozilla Common Voice (v12.0)  - 708 hour subset
+- Multilingual Librispeech (MLS FR) - 926 hour subset
+- VoxPopuli (FR) - 165 hour subset
+
+
+## Performance
+
+In both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.
+
+### ASR Performance (w/o PnC) 
+
+The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with [whisper-normalizer](https://pypi.org/project/whisper-normalizer/).
+
+WER on [MCV-16.1](https://commonvoice.mozilla.org/en/datasets) test set:
+
+| **Version** | **Model**     | **En**   | **De**   | **Es**   | **Fr**   |
+|:---------:|:-----------:|:------:|:------:|:------:|:------:|
+| 1.23.0  | canary-1b | 7.97 | 4.61 | 3.99 | 6.53 |
+
+
+WER on [MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech) test set:
+
+| **Version** | **Model**     | **En**   | **De**   | **Es**   | **Fr**   |
+|:---------:|:-----------:|:------:|:------:|:------:|:------:|
+| 1.23.0  | canary-1b | 3.06 | 4.19 | 3.15 | 4.12 |
+
+
+More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
+
+### AST Performance
+
+We evaluate AST performance with [BLEU score](https://lightning.ai/docs/torchmetrics/stable/text/sacre_bleu_score.html), and use native annotations with punctuation and capitalization in the datasets.
+
+BLEU score on [FLEURS](https://huggingface.co/datasets/google/fleurs) test set:
+
+| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | **De->En** | **Es->En** | **Fr->En** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 32.15	   | 22.66      | 40.76      | 33.98      | 21.80      | 30.95      |
+
+
+BLEU score on [COVOST-v2](https://github.com/facebookresearch/covost) test set:
+
+| **Version** | **Model** | **De->En** | **Es->En** | **Fr->En** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 37.67      | 40.7       | 40.42      |
+
+BLEU score on [mExpresso](https://huggingface.co/facebook/seamless-expressive#mexpresso-multilingual-expresso) test set:
+
+| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 23.84      |   35.74    | 28.29      |
+
+## Model Fairness Evaluation
+
+As outlined in the paper ""Towards Measuring Fairness in AI: the Casual Conversations Dataset"", we assessed the Canary-1B model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:
+
+### Gender Bias:
+
+| Gender | Male | Female | N/A | Other |
+| :--- | :--- | :--- | :--- | :--- |
+| Num utterances | 19325 | 24532 | 926 | 33 |
+| % WER | 14.64 | 12.92 | 17.88 | 126.92 |
+
+### Age Bias:
+
+| Age Group | (18-30) | (31-45) | (46-85) | (1-100) |
+| :--- | :--- | :--- | :--- | :--- |
+| Num utterances | 15956 | 14585 | 13349 | 43890 |
+| % WER | 14.64 | 13.07 | 13.47 | 13.76 |
+
+(Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
+
+## NVIDIA Riva: Deployment
+
+[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. 
+Additionally, Riva provides: 
+
+* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
+* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization 
+* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support 
+
+Canary is available as a NIM endpoint via Riva. Try the model yourself here: [https://build.nvidia.com/nvidia/canary-1b-asr](https://build.nvidia.com/nvidia/canary-1b-asr). 
+
+
+## References
+[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
+
+[2] [Attention is all you need](https://arxiv.org/abs/1706.03762)
+
+[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
+
+[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+[5] [Unified Model for Code-Switching Speech Recognition and Language Identification Based on Concatenated Tokenizer](https://aclanthology.org/2023.calcs-1.7.pdf)
+
+## Licence
+
+License to use this model is covered by the [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en#:~:text=NonCommercial%20%E2%80%94%20You%20may%20not%20use,doing%20anything%20the%20license%20permits.). By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license.","{""id"": ""nvidia/canary-1b"", ""author"": ""nvidia"", ""sha"": ""d68d53f1b0f6e45d528edbb0b8e247f28d476a7e"", ""last_modified"": ""2025-04-24 00:04:08+00:00"", ""created_at"": ""2024-02-07 17:20:55+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 9714, ""downloads_all_time"": null, ""likes"": 421, ""library_name"": ""nemo"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""nemo"", ""automatic-speech-recognition"", ""automatic-speech-translation"", ""speech"", ""audio"", ""Transformer"", ""FastConformer"", ""Conformer"", ""pytorch"", ""NeMo"", ""hf-asr-leaderboard"", ""en"", ""de"", ""es"", ""fr"", ""dataset:librispeech_asr"", ""dataset:fisher_corpus"", ""dataset:Switchboard-1"", ""dataset:WSJ-0"", ""dataset:WSJ-1"", ""dataset:National-Singapore-Corpus-Part-1"", ""dataset:National-Singapore-Corpus-Part-6"", ""dataset:vctk"", ""dataset:voxpopuli"", ""dataset:europarl"", ""dataset:multilingual_librispeech"", ""dataset:mozilla-foundation/common_voice_8_0"", ""dataset:MLCommons/peoples_speech"", ""arxiv:2305.05084"", ""arxiv:1706.03762"", ""license:cc-by-nc-4.0"", ""model-index"", ""region:us""], ""pipeline_tag"": ""automatic-speech-recognition"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""datasets:\n- librispeech_asr\n- fisher_corpus\n- Switchboard-1\n- WSJ-0\n- WSJ-1\n- National-Singapore-Corpus-Part-1\n- National-Singapore-Corpus-Part-6\n- vctk\n- voxpopuli\n- europarl\n- multilingual_librispeech\n- mozilla-foundation/common_voice_8_0\n- MLCommons/peoples_speech\nlanguage:\n- en\n- de\n- es\n- fr\nlibrary_name: nemo\nlicense: cc-by-nc-4.0\nmetrics:\n- wer\n- bleu\npipeline_tag: automatic-speech-recognition\ntags:\n- automatic-speech-recognition\n- automatic-speech-translation\n- speech\n- audio\n- Transformer\n- FastConformer\n- Conformer\n- pytorch\n- NeMo\n- hf-asr-leaderboard\nwidget:\n- example_title: Librispeech sample 1\n  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac\n- example_title: Librispeech sample 2\n  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac\nmodel-index:\n- name: canary-1b\n  results:\n  - task:\n      type: automatic-speech-recognition\n      name: Automatic Speech Recognition\n    dataset:\n      name: LibriSpeech (other)\n      type: librispeech_asr\n      config: other\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 2.89\n      name: Test WER\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: SPGI Speech\n      type: kensho/spgispeech\n      config: test\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 4.79\n      name: Test WER\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: en\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 7.97\n      name: Test WER (En)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: de\n      split: test\n      args:\n        language: de\n    metrics:\n    - type: wer\n      value: 4.61\n      name: Test WER (De)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: es\n      split: test\n      args:\n        language: es\n    metrics:\n    - type: wer\n      value: 3.99\n      name: Test WER (ES)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: fr\n      split: test\n      args:\n        language: fr\n    metrics:\n    - type: wer\n      value: 6.53\n      name: Test WER (Fr)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: en_us\n      split: test\n      args:\n        language: en-de\n    metrics:\n    - type: bleu\n      value: 32.15\n      name: Test BLEU (En->De)\n      verified: false\n    - type: bleu\n      value: 22.66\n      name: Test BLEU (En->Es)\n      verified: false\n    - type: bleu\n      value: 40.76\n      name: Test BLEU (En->Fr)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: de_de\n      split: test\n      args:\n        language: de-en\n    metrics:\n    - type: bleu\n      value: 33.98\n      name: Test BLEU (De->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: es_419\n      split: test\n      args:\n        language: es-en\n    metrics:\n    - type: bleu\n      value: 21.8\n      name: Test BLEU (Es->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: fr_fr\n      split: test\n      args:\n        language: fr-en\n    metrics:\n    - type: bleu\n      value: 30.95\n      name: Test BLEU (Fr->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: de_de\n      split: test\n      args:\n        language: de-en\n    metrics:\n    - type: bleu\n      value: 37.67\n      name: Test BLEU (De->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: es_419\n      split: test\n      args:\n        language: es-en\n    metrics:\n    - type: bleu\n      value: 40.7\n      name: Test BLEU (Es->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: fr_fr\n      split: test\n      args:\n        language: fr-en\n    metrics:\n    - type: bleu\n      value: 40.42\n      name: Test BLEU (Fr->En)\n      verified: false"", ""widget_data"": [{""example_title"": ""Librispeech sample 1"", ""src"": ""https://cdn-media.huggingface.co/speech_samples/sample1.flac""}, {""example_title"": ""Librispeech sample 2"", ""src"": ""https://cdn-media.huggingface.co/speech_samples/sample2.flac""}], ""model_index"": [{""name"": ""canary-1b"", ""results"": [{""task"": {""name"": ""Automatic Speech Recognition"", ""type"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""LibriSpeech (other)"", ""type"": ""librispeech_asr"", ""config"": ""other"", ""split"": ""test"", ""args"": {""language"": ""en""}}, ""metrics"": [{""name"": ""Test WER"", ""type"": ""wer"", ""value"": 2.89, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Recognition"", ""name"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""SPGI Speech"", ""type"": ""kensho/spgispeech"", ""config"": ""test"", ""split"": ""test"", ""args"": {""language"": ""en""}}, ""metrics"": [{""name"": ""Test WER"", ""type"": ""wer"", ""value"": 4.79, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Recognition"", ""name"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""Mozilla Common Voice 16.1"", ""type"": ""mozilla-foundation/common_voice_16_1"", ""config"": ""en"", ""split"": ""test"", ""args"": {""language"": ""en""}}, ""metrics"": [{""name"": ""Test WER (En)"", ""type"": ""wer"", ""value"": 7.97, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Recognition"", ""name"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""Mozilla Common Voice 16.1"", ""type"": ""mozilla-foundation/common_voice_16_1"", ""config"": ""de"", ""split"": ""test"", ""args"": {""language"": ""de""}}, ""metrics"": [{""name"": ""Test WER (De)"", ""type"": ""wer"", ""value"": 4.61, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Recognition"", ""name"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""Mozilla Common Voice 16.1"", ""type"": ""mozilla-foundation/common_voice_16_1"", ""config"": ""es"", ""split"": ""test"", ""args"": {""language"": ""es""}}, ""metrics"": [{""name"": ""Test WER (ES)"", ""type"": ""wer"", ""value"": 3.99, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Recognition"", ""name"": ""automatic-speech-recognition""}, ""dataset"": {""name"": ""Mozilla Common Voice 16.1"", ""type"": ""mozilla-foundation/common_voice_16_1"", ""config"": ""fr"", ""split"": ""test"", ""args"": {""language"": ""fr""}}, ""metrics"": [{""name"": ""Test WER (Fr)"", ""type"": ""wer"", ""value"": 6.53, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""en_us"", ""split"": ""test"", ""args"": {""language"": ""en-de""}}, ""metrics"": [{""name"": ""Test BLEU (En->De)"", ""type"": ""bleu"", ""value"": 32.15, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""en_us"", ""split"": ""test"", ""args"": {""language"": ""en-de""}}, ""metrics"": [{""name"": ""Test BLEU (En->Es)"", ""type"": ""bleu"", ""value"": 22.66, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""en_us"", ""split"": ""test"", ""args"": {""language"": ""en-de""}}, ""metrics"": [{""name"": ""Test BLEU (En->Fr)"", ""type"": ""bleu"", ""value"": 40.76, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""de_de"", ""split"": ""test"", ""args"": {""language"": ""de-en""}}, ""metrics"": [{""name"": ""Test BLEU (De->En)"", ""type"": ""bleu"", ""value"": 33.98, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""es_419"", ""split"": ""test"", ""args"": {""language"": ""es-en""}}, ""metrics"": [{""name"": ""Test BLEU (Es->En)"", ""type"": ""bleu"", ""value"": 21.8, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""FLEURS"", ""type"": ""google/fleurs"", ""config"": ""fr_fr"", ""split"": ""test"", ""args"": {""language"": ""fr-en""}}, ""metrics"": [{""name"": ""Test BLEU (Fr->En)"", ""type"": ""bleu"", ""value"": 30.95, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""COVOST"", ""type"": ""covost2"", ""config"": ""de_de"", ""split"": ""test"", ""args"": {""language"": ""de-en""}}, ""metrics"": [{""name"": ""Test BLEU (De->En)"", ""type"": ""bleu"", ""value"": 37.67, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""COVOST"", ""type"": ""covost2"", ""config"": ""es_419"", ""split"": ""test"", ""args"": {""language"": ""es-en""}}, ""metrics"": [{""name"": ""Test BLEU (Es->En)"", ""type"": ""bleu"", ""value"": 40.7, ""verified"": false}]}, {""task"": {""type"": ""Automatic Speech Translation"", ""name"": ""automatic-speech-translation""}, ""dataset"": {""name"": ""COVOST"", ""type"": ""covost2"", ""config"": ""fr_fr"", ""split"": ""test"", ""args"": {""language"": ""fr-en""}}, ""metrics"": [{""name"": ""Test BLEU (Fr->En)"", ""type"": ""bleu"", ""value"": 40.42, ""verified"": false}]}]}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='canary-1b.nemo', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='canary-model.png', size=None, blob_id=None, lfs=None)""], ""spaces"": [""nvidia/canary-1b"", ""awacke1/Speech-Recognition-Canary-NvidiaT4"", ""gobeldan/canary-1b"", ""jiuuee/my-alexa"", ""almncarlo/myalexa"", ""gdnartea/Chatty_Ashe"", ""VanYsa/MyAlexa"", ""drc97/nvidia-canary-1b"", ""monuene/nvidia-canary-1b"", ""lnajdi/nvidia-canary-1b"", ""Nymbo/canary-1b-TTS-Translate"", ""xeeshanakram/nvidia-canary-1b"", ""bqmolina/MyAlexa"", ""Kindler/197zAlexa"", ""notabaka/nvidia-canary-1b"", ""micoirvin-up/sample-my-alexa"", ""jlcastalla/MyAlexa"", ""ashamshur/nvidia-canary-1b"", ""jdlugo/nvidia-canary-1b-docker"", ""Oreobird/nvidia-canary-1b"", ""rayl-aoit/rayl-gradio-labs"", ""Flymark8/nvidia-canary-1b"", ""cottom/nvidia-canary-1b"", ""rayl-aoit/translate_text_and_speech"", ""umarigan/speech-to-speech-translation"", ""Diego055/translate_text_and_speech"", ""Gyaneshere/Canary-Speech-to-text""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-04-24 00:04:08+00:00"", ""cardData"": ""datasets:\n- librispeech_asr\n- fisher_corpus\n- Switchboard-1\n- WSJ-0\n- WSJ-1\n- National-Singapore-Corpus-Part-1\n- National-Singapore-Corpus-Part-6\n- vctk\n- voxpopuli\n- europarl\n- multilingual_librispeech\n- mozilla-foundation/common_voice_8_0\n- MLCommons/peoples_speech\nlanguage:\n- en\n- de\n- es\n- fr\nlibrary_name: nemo\nlicense: cc-by-nc-4.0\nmetrics:\n- wer\n- bleu\npipeline_tag: automatic-speech-recognition\ntags:\n- automatic-speech-recognition\n- automatic-speech-translation\n- speech\n- audio\n- Transformer\n- FastConformer\n- Conformer\n- pytorch\n- NeMo\n- hf-asr-leaderboard\nwidget:\n- example_title: Librispeech sample 1\n  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac\n- example_title: Librispeech sample 2\n  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac\nmodel-index:\n- name: canary-1b\n  results:\n  - task:\n      type: automatic-speech-recognition\n      name: Automatic Speech Recognition\n    dataset:\n      name: LibriSpeech (other)\n      type: librispeech_asr\n      config: other\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 2.89\n      name: Test WER\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: SPGI Speech\n      type: kensho/spgispeech\n      config: test\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 4.79\n      name: Test WER\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: en\n      split: test\n      args:\n        language: en\n    metrics:\n    - type: wer\n      value: 7.97\n      name: Test WER (En)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: de\n      split: test\n      args:\n        language: de\n    metrics:\n    - type: wer\n      value: 4.61\n      name: Test WER (De)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: es\n      split: test\n      args:\n        language: es\n    metrics:\n    - type: wer\n      value: 3.99\n      name: Test WER (ES)\n      verified: false\n  - task:\n      type: Automatic Speech Recognition\n      name: automatic-speech-recognition\n    dataset:\n      name: Mozilla Common Voice 16.1\n      type: mozilla-foundation/common_voice_16_1\n      config: fr\n      split: test\n      args:\n        language: fr\n    metrics:\n    - type: wer\n      value: 6.53\n      name: Test WER (Fr)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: en_us\n      split: test\n      args:\n        language: en-de\n    metrics:\n    - type: bleu\n      value: 32.15\n      name: Test BLEU (En->De)\n      verified: false\n    - type: bleu\n      value: 22.66\n      name: Test BLEU (En->Es)\n      verified: false\n    - type: bleu\n      value: 40.76\n      name: Test BLEU (En->Fr)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: de_de\n      split: test\n      args:\n        language: de-en\n    metrics:\n    - type: bleu\n      value: 33.98\n      name: Test BLEU (De->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: es_419\n      split: test\n      args:\n        language: es-en\n    metrics:\n    - type: bleu\n      value: 21.8\n      name: Test BLEU (Es->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: FLEURS\n      type: google/fleurs\n      config: fr_fr\n      split: test\n      args:\n        language: fr-en\n    metrics:\n    - type: bleu\n      value: 30.95\n      name: Test BLEU (Fr->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: de_de\n      split: test\n      args:\n        language: de-en\n    metrics:\n    - type: bleu\n      value: 37.67\n      name: Test BLEU (De->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: es_419\n      split: test\n      args:\n        language: es-en\n    metrics:\n    - type: bleu\n      value: 40.7\n      name: Test BLEU (Es->En)\n      verified: false\n  - task:\n      type: Automatic Speech Translation\n      name: automatic-speech-translation\n    dataset:\n      name: COVOST\n      type: covost2\n      config: fr_fr\n      split: test\n      args:\n        language: fr-en\n    metrics:\n    - type: bleu\n      value: 40.42\n      name: Test BLEU (Fr->En)\n      verified: false"", ""transformersInfo"": null, ""_id"": ""65c3bbf780497543ca174a16"", ""modelId"": ""nvidia/canary-1b"", ""usedStorage"": 8352624640}",0,,0,,0,,0,,0,"Nymbo/canary-1b-TTS-Translate, VanYsa/MyAlexa, almncarlo/myalexa, ashamshur/nvidia-canary-1b, awacke1/Speech-Recognition-Canary-NvidiaT4, drc97/nvidia-canary-1b, gdnartea/Chatty_Ashe, gobeldan/canary-1b, hf-audio/open_asr_leaderboard, huggingface/InferenceSupport/discussions/new?title=nvidia/canary-1b&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bnvidia%2Fcanary-1b%5D(%2Fnvidia%2Fcanary-1b)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, jiuuee/my-alexa, lnajdi/nvidia-canary-1b, monuene/nvidia-canary-1b, nvidia/canary-1b",14

chatglm3-6b-32k_finetunes_20250426_171734.csv_finetunes_20250426_171734.csv

@@ -0,0 +1,113 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+THUDM/chatglm3-6b-32k,"---
+language:
+- zh
+- en
+tags:
+- glm
+- chatglm
+- thudm
+---
+# ChatGLM3-6B-32K
+<p align=""center"">
+  💻 <a href=""https://github.com/THUDM/ChatGLM"" target=""_blank"">Github Repo</a> • 🐦 <a href=""https://twitter.com/thukeg"" target=""_blank"">Twitter</a> • 📃 <a href=""https://arxiv.org/abs/2103.10360"" target=""_blank"">[GLM@ACL 22]</a> <a href=""https://github.com/THUDM/GLM"" target=""_blank"">[GitHub]</a> • 📃 <a href=""https://arxiv.org/abs/2210.02414"" target=""_blank"">[GLM-130B@ICLR 23]</a> <a href=""https://github.com/THUDM/GLM-130B"" target=""_blank"">[GitHub]</a> <br>
+</p>
+
+<p align=""center"">
+    👋 Join our <a href=""https://join.slack.com/t/chatglm/shared_invite/zt-25ti5uohv-A_hs~am_D3Q8XPZMpj7wwQ"" target=""_blank"">Slack</a> and <a href=""https://github.com/THUDM/ChatGLM/blob/main/resources/WECHAT.md"" target=""_blank"">WeChat</a>
+</p>
+<p align=""center"">
+📍Experience the larger-scale ChatGLM model at <a href=""https://www.chatglm.cn"">chatglm.cn</a>
+</p>
+
+## 介绍 (Introduction)
+
+ChatGLM3-6B-32K在[ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b)的基础上进一步强化了对于长文本的理解能力，能够更好的处理最多32K长度的上下文。具体地，我们对位置编码进行了更新，并设计了更有针对性的长文本训练方法，在对话阶段使用 32K 的上下文长度训练。在实际的使用中，如果您面临的上下文长度基本在 **8K 以内**，我们推荐使用[ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b)；如果您需要处理**超过 8K** 的上下文长度，我们推荐使用ChatGLM3-6B-32K。
+
+ChatGLM3-6B 是 ChatGLM 系列最新一代的开源模型，在保留了前两代模型对话流畅、部署门槛低等众多优秀特性的基础上，ChatGLM3-6B 引入了如下特性：
+
+1. **更强大的基础模型：** ChatGLM3-6B 的基础模型 ChatGLM3-6B-Base 采用了更多样的训练数据、更充分的训练步数和更合理的训练策略。在语义、数学、推理、代码、知识等不同角度的数据集上测评显示，ChatGLM3-6B-Base 具有在 10B 以下的预训练模型中最强的性能。
+2. **更完整的功能支持：** ChatGLM3-6B 采用了全新设计的 [Prompt 格式](https://github.com/THUDM/ChatGLM3/blob/main/README.md)，除正常的多轮对话外。同时原生支持[工具调用](https://github.com/THUDM/ChatGLM3/blob/main/tools_using_demo/README.md)（Function Call）、代码执行（Code Interpreter）和 Agent 任务等复杂场景。
+3. **更全面的开源序列：** 除了对话模型 ChatGLM3-6B 外，还开源了基础模型 ChatGLM-6B-Base、长文本对话模型 ChatGLM3-6B-32K。以上所有权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。
+
+Based on [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b), ChatGLM3-6B-32K further strengthens the ability to understand long texts and can better handle contexts up to 32K in length. Specifically, we update the position encoding and design a more targeted long text training method, using a context length of 32K for training in the conversation stage. In actual use, if the context length you face is basically within **8K**, we recommend using [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b); if you need to handle **For context lengths exceeding 8K**, we recommend using ChatGLM3-6B-32K.
+
+ChatGLM3-6B is the latest open-source model in the ChatGLM series. While retaining many excellent features such as smooth dialogue and low deployment threshold from the previous two generations, ChatGLM3-6B introduces the following features:
+
+1. **More Powerful Base Model:** The base model of ChatGLM3-6B, ChatGLM3-6B-Base, employs a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy. Evaluations on datasets such as semantics, mathematics, reasoning, code, knowledge, etc., show that ChatGLM3-6B-Base has the strongest performance among pre-trained models under 10B.
+2. **More Comprehensive Function Support:** ChatGLM3-6B adopts a newly designed [Prompt format](https://github.com/THUDM/ChatGLM3/blob/main/PROMPT_en.md), in addition to the normal multi-turn dialogue. It also natively supports [function call](https://github.com/THUDM/ChatGLM3/blob/main/tools_using_demo/README.md), code interpreter, and complex scenarios such as agent tasks.
+3. **More Comprehensive Open-source Series:** In addition to the dialogue model ChatGLM3-6B, the base model ChatGLM-6B-Base and the long-text dialogue model ChatGLM3-6B-32K are also open-sourced. All the weights are **fully open** for academic research, and after completing the [questionnaire](https://open.bigmodel.cn/mla/form) registration, they are also **allowed for free commercial use**.
+
+
+## 软件依赖 (Dependencies)
+
+```shell
+pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate
+```
+
+## 代码调用 (Code Usage)
+
+可以通过如下代码调用 ChatGLM3-6B 模型来生成对话：
+
+```ipython
+>>> from transformers import AutoTokenizer, AutoModel
+>>> tokenizer = AutoTokenizer.from_pretrained(""THUDM/chatglm3-6b-32k"", trust_remote_code=True)
+>>> model = AutoModel.from_pretrained(""THUDM/chatglm3-6b-32k"", trust_remote_code=True).half().cuda()
+>>> model = model.eval()
+>>> response, history = model.chat(tokenizer, ""你好"", history=[])
+>>> print(response)
+你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
+>>> response, history = model.chat(tokenizer, ""晚上睡不着应该怎么办"", history=history)
+>>> print(response)
+晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
+
+1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
+2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
+3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
+4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
+5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
+6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
+
+如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
+```
+
+关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM)。
+
+For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM).
+
+
+## 协议 (License)
+
+本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM3-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
+
+The code in this repository is open-sourced under the [Apache-2.0 license](LICENSE), while the use of the ChatGLM3-6B model weights needs to comply with the [Model License](MODEL_LICENSE).
+
+## 引用 (Citation)
+
+如果你觉得我们的工作有帮助的话，请考虑引用下列论文。
+
+If you find our work helpful, please consider citing the following paper.
+
+```
+@misc{glm2024chatglm,
+      title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, 
+      author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
+      year={2024},
+      eprint={2406.12793},
+      archivePrefix={arXiv},
+      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+}
+```
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+Tridefender/chatglm3_6b_32k_TensorRTReady,"---
+license: apache-2.0
+base_model:
+- THUDM/chatglm3-6b-32k
+tags:
+- text-generation-inference
+language:
+- zh
+pipeline_tag: text-generation
+library_name: transformers
+---
+This is a derived model of chatglm3-6b-32k, has been converted to TensorRT LLM checkpoint for further usage. The model is presented in different quantizations.","{""id"": ""Tridefender/chatglm3_6b_32k_TensorRTReady"", ""author"": ""Tridefender"", ""sha"": ""49da6ea4dd1a58138ff18b4a24682deb6b467103"", ""last_modified"": ""2024-12-05 19:09:41+00:00"", ""created_at"": ""2024-12-03 14:47:46+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""text-generation-inference"", ""text-generation"", ""zh"", ""base_model:THUDM/chatglm3-6b-32k"", ""base_model:finetune:THUDM/chatglm3-6b-32k"", ""license:apache-2.0"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- THUDM/chatglm3-6b-32k\nlanguage:\n- zh\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: text-generation\ntags:\n- text-generation-inference"", ""widget_data"": [{""text"": ""\u6211\u53eb\u6731\u5229\u5b89\uff0c\u6211\u559c\u6b22""}, {""text"": ""\u6211\u53eb\u6258\u9a6c\u65af\uff0c\u6211\u7684\u4e3b\u8981""}, {""text"": ""\u6211\u53eb\u739b\u4e3d\u4e9a\uff0c\u6211\u6700\u559c\u6b22\u7684""}, {""text"": ""\u6211\u53eb\u514b\u62c9\u62c9\uff0c\u6211\u662f""}, {""text"": ""\u4ece\u524d\uff0c""}], ""model_index"": null, ""config"": null, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='fp16/1-gpu/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='fp16/1-gpu/rank0.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='int4_weight_only/1-gpu/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='int4_weight_only/1-gpu/rank0.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='int8_weight_only/1-gpu/config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='int8_weight_only/1-gpu/rank0.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-12-05 19:09:41+00:00"", ""cardData"": ""base_model:\n- THUDM/chatglm3-6b-32k\nlanguage:\n- zh\nlibrary_name: transformers\nlicense: apache-2.0\npipeline_tag: text-generation\ntags:\n- text-generation-inference"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""674f1a12c8b2fa1eded3f985"", ""modelId"": ""Tridefender/chatglm3_6b_32k_TensorRTReady"", ""usedStorage"": 23240072216}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=Tridefender/chatglm3_6b_32k_TensorRTReady&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BTridefender%2Fchatglm3_6b_32k_TensorRTReady%5D(%2FTridefender%2Fchatglm3_6b_32k_TensorRTReady)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

deepseek-coder-33b-instruct_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,247 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+deepseek-ai/deepseek-coder-33b-instruct,"---
+license: other
+license_name: deepseek
+license_link: LICENSE
+---
+
+
+<p align=""center"">
+<img width=""1000px"" alt=""DeepSeek Coder"" src=""https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"">
+</p>
+<p align=""center""><a href=""https://www.deepseek.com/"">[🏠Homepage]</a>  |  <a href=""https://coder.deepseek.com/"">[🤖 Chat with DeepSeek Coder]</a>  |  <a href=""https://discord.gg/Tc7c45Zzu5"">[Discord]</a>  |  <a href=""https://github.com/guoday/assert/blob/main/QR.png?raw=true"">[Wechat(微信)]</a> </p>
+<hr>
+
+
+
+### 1. Introduction of Deepseek Coder
+
+Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support  project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks. 
+
+- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
+  
+- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
+  
+- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
+  
+- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
+
+ 
+  
+### 2. Model Summary
+deepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data.
+- **Home Page:** [DeepSeek](https://deepseek.com/)
+- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
+- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
+
+
+### 3. How to Use
+Here give some examples of how to use our model.
+#### Chat Model Inference
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained(""deepseek-ai/deepseek-coder-6.7b-instruct"", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(""deepseek-ai/deepseek-coder-6.7b-instruct"", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
+messages=[
+    { 'role': 'user', 'content': ""write a quick sort algorithm in python.""}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors=""pt"").to(model.device)
+# tokenizer.eos_token_id is the id of <|EOT|> token
+outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
+```
+
+### 4. License
+This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
+
+See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
+
+### 5. Contact
+
+If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
+
+","{""id"": ""deepseek-ai/deepseek-coder-33b-instruct"", ""author"": ""deepseek-ai"", ""sha"": ""61dc97b922b13995e7f83b7c8397701dbf9cfd4c"", ""last_modified"": ""2024-03-07 08:25:20+00:00"", ""created_at"": ""2023-11-01 05:46:34+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 8622, ""downloads_all_time"": null, ""likes"": 509, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""safetensors"", ""llama"", ""text-generation"", ""conversational"", ""license:other"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: other\nlicense_name: deepseek\nlicense_link: LICENSE"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""<|EOT|>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""pad_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""unk_token"": null, ""chat_template"": ""{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='LICENSE', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", 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https://huggingface.co/ai-agnisys/ids-integrate-model",4,"https://huggingface.co/Ngit/mnv-deepseek33b, https://huggingface.co/NikitaZagainov/notebook-generation-deepseek-33b-2ep, https://huggingface.co/NikitaZagainov/notebook-generation-deepseek-33b-3ep, https://huggingface.co/NikitaZagainov/notebook-generation-deepseek-33b-1ep, https://huggingface.co/NikitaZagainov/notebook-generation-deepseek-33b-4ep, https://huggingface.co/NikitaZagainov/notebook-generation-deepseek-33b-5ep",6,"https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF, https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-AWQ, https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GPTQ, https://huggingface.co/altomek/deepseek-coder-33b-instruct-8bpw-EXL2, https://huggingface.co/mradermacher/deepseek-coder-33b-instruct-GGUF, https://huggingface.co/mradermacher/deepseek-coder-33b-instruct-i1-GGUF, https://huggingface.co/tensorblock/deepseek-coder-33b-instruct-GGUF, https://huggingface.co/TFSID/deepseek-coder-33b-instruct-Q4_K_M-GGUF",8,"https://huggingface.co/smokewulf/Code-01, https://huggingface.co/arvindanand/Deepseek-Wizard-33B-slerp",2,"21world/bigcode-models-leaderboard, FallnAI/Quantize-HF-Models, HPAI-BSC/TuRTLe-Leaderboard, Justinrune/LLaMA-Factory, KBaba7/Quant, Sarath0x8f/Document-QA-bot, bardsai/performance-llm-board, bhaskartripathi/LLM_Quantization, bigcode/bigcode-models-leaderboard, deepseek-ai/deepseek-coder-33b-instruct, huggingface/InferenceSupport/discussions/new?title=deepseek-ai/deepseek-coder-33b-instruct&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bdeepseek-ai%2Fdeepseek-coder-33b-instruct%5D(%2Fdeepseek-ai%2Fdeepseek-coder-33b-instruct)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, kenken999/fastapi_django_main_live, yhavinga/dutch-tokenizer-arena",13
+arvindanand/ValidateAI-2-33B-AT,"---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- WizardLM/WizardCoder-33B-V1.1
+- codefuse-ai/CodeFuse-DeepSeek-33B
+- deepseek-ai/deepseek-coder-33b-instruct
+
+base_model:
+- deepseek-ai/deepseek-coder-33b-instruct
+
+license: apache-2.0
+---
+
+# ValidateAI-2-33B-AT
+
+ ValidateAI-2-33B-AT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [deepseek-ai/deepseek-coder-33b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct)
+* [WizardLM/WizardCoder-33B-V1.1](https://huggingface.co/WizardLM/WizardCoder-33B-V1.1)
+* [codefuse-ai/CodeFuse-DeepSeek-33B](https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B)
+* 
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: codefuse-ai_CodeFuse-DeepSeek-33B
+    parameters:
+      weight: 1
+  - model: deepseek-ai_deepseek-coder-33b-instruct
+    parameters:
+      weight: 1
+  - model: WizardLM_WizardCoder-33B-V1.1
+    parameters:
+      weight: 1
+merge_method: task_arithmetic
+base_model: deepseek-ai_deepseek-coder-33b-base
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = ""arvindanand/ValidateAI-2-33B-AT""
+messages = [{""role"": ""user"", ""content"": ""What is a large language model?""}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    ""text-generation"",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map=""auto"",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0][""generated_text""])
+```
+
+
+","{""id"": ""arvindanand/ValidateAI-2-33B-AT"", ""author"": ""arvindanand"", ""sha"": ""8ced5df95de3d9bf4052028cd02fd52c1f9a5256"", ""last_modified"": ""2024-04-16 02:06:40+00:00"", ""created_at"": ""2024-04-11 07:55:04+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 12, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""merge"", ""mergekit"", ""lazymergekit"", ""WizardLM/WizardCoder-33B-V1.1"", ""codefuse-ai/CodeFuse-DeepSeek-33B"", ""deepseek-ai/deepseek-coder-33b-instruct"", ""base_model:deepseek-ai/deepseek-coder-33b-instruct"", ""base_model:finetune:deepseek-ai/deepseek-coder-33b-instruct"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- deepseek-ai/deepseek-coder-33b-instruct\nlicense: apache-2.0\ntags:\n- merge\n- mergekit\n- lazymergekit\n- WizardLM/WizardCoder-33B-V1.1\n- codefuse-ai/CodeFuse-DeepSeek-33B\n- deepseek-ai/deepseek-coder-33b-instruct"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""pad_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""unk_token"": null}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00007-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""F16"": 33342991360}, ""total"": 33342991360}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-04-16 02:06:40+00:00"", ""cardData"": ""base_model:\n- deepseek-ai/deepseek-coder-33b-instruct\nlicense: apache-2.0\ntags:\n- merge\n- mergekit\n- lazymergekit\n- WizardLM/WizardCoder-33B-V1.1\n- codefuse-ai/CodeFuse-DeepSeek-33B\n- deepseek-ai/deepseek-coder-33b-instruct"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""66179758feb710dbf16d9a23"", ""modelId"": ""arvindanand/ValidateAI-2-33B-AT"", ""usedStorage"": 66686047680}",1,,0,,0,"https://huggingface.co/mradermacher/ValidateAI-2-33B-AT-GGUF, https://huggingface.co/mradermacher/ValidateAI-2-33B-AT-i1-GGUF",2,,0,huggingface/InferenceSupport/discussions/new?title=arvindanand/ValidateAI-2-33B-AT&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Barvindanand%2FValidateAI-2-33B-AT%5D(%2Farvindanand%2FValidateAI-2-33B-AT)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+arvindanand/ValidateAI-3-33B-Ties,"---
+tags:
+- merge
+- mergekit
+- lazymergekit
+- WizardLM/WizardCoder-33B-V1.1
+- codefuse-ai/CodeFuse-DeepSeek-33B
+- deepseek-ai/deepseek-coder-33b-instruct
+
+base_model:
+- deepseek-ai/deepseek-coder-33b-instruct
+
+license: apache-2.0
+---
+
+# ValidateAI-2-33B-Ties
+
+ ValidateAI-2-33B-Ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [deepseek-ai/deepseek-coder-33b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct)
+* [WizardLM/WizardCoder-33B-V1.1](https://huggingface.co/WizardLM/WizardCoder-33B-V1.1)
+* [codefuse-ai/CodeFuse-DeepSeek-33B](https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B)
+* 
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: WizardLM_WizardCoder-33B-V1.1
+    parameters:
+      density: 1
+      weight: .5
+  - model: codefuse-ai_CodeFuse-DeepSeek-33B
+    parameters:
+      density: 1
+      weight: .5
+merge_method: ties
+base_model: deepseek-ai_deepseek-coder-33b-instruct
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = ""arvindanand/ValidateAI-3-33B-Ties""
+messages = [{""role"": ""user"", ""content"": ""What is a large language model?""}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    ""text-generation"",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map=""auto"",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0][""generated_text""])
+```","{""id"": ""arvindanand/ValidateAI-3-33B-Ties"", ""author"": ""arvindanand"", ""sha"": ""f556cfcf87f5ab1a88a296a9fa155be369c2f291"", ""last_modified"": ""2024-04-11 19:19:20+00:00"", ""created_at"": ""2024-04-11 19:18:25+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 10, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""llama"", ""text-generation"", ""merge"", ""mergekit"", ""lazymergekit"", ""WizardLM/WizardCoder-33B-V1.1"", ""codefuse-ai/CodeFuse-DeepSeek-33B"", ""deepseek-ai/deepseek-coder-33b-instruct"", ""conversational"", ""base_model:deepseek-ai/deepseek-coder-33b-instruct"", ""base_model:finetune:deepseek-ai/deepseek-coder-33b-instruct"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- deepseek-ai/deepseek-coder-33b-instruct\nlicense: apache-2.0\ntags:\n- merge\n- mergekit\n- lazymergekit\n- WizardLM/WizardCoder-33B-V1.1\n- codefuse-ai/CodeFuse-DeepSeek-33B\n- deepseek-ai/deepseek-coder-33b-instruct"", ""widget_data"": [{""text"": ""Hi, what can you help me with?""}, {""text"": ""What is 84 * 3 / 2?""}, {""text"": ""Tell me an interesting fact about the universe!""}, {""text"": ""Explain quantum computing in simple terms.""}], ""model_index"": null, ""config"": {""architectures"": [""LlamaForCausalLM""], ""model_type"": ""llama"", ""tokenizer_config"": {""bos_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""eos_token"": {""__type"": ""AddedToken"", ""content"": ""<|EOT|>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""pad_token"": {""__type"": ""AddedToken"", ""content"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""lstrip"": false, ""normalized"": true, ""rstrip"": false, ""single_word"": false}, ""unk_token"": null, ""chat_template"": ""{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00001-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00002-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00003-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00004-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00005-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00006-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model-00007-of-00007.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors.index.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""F16"": 33342991360}, ""total"": 33342991360}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-04-11 19:19:20+00:00"", ""cardData"": ""base_model:\n- deepseek-ai/deepseek-coder-33b-instruct\nlicense: apache-2.0\ntags:\n- merge\n- mergekit\n- lazymergekit\n- WizardLM/WizardCoder-33B-V1.1\n- codefuse-ai/CodeFuse-DeepSeek-33B\n- deepseek-ai/deepseek-coder-33b-instruct"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""661837812a39149b64d577cc"", ""modelId"": ""arvindanand/ValidateAI-3-33B-Ties"", ""usedStorage"": 66686047680}",1,,0,,0,https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF,1,,0,huggingface/InferenceSupport/discussions/new?title=arvindanand/ValidateAI-3-33B-Ties&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Barvindanand%2FValidateAI-3-33B-Ties%5D(%2Farvindanand%2FValidateAI-3-33B-Ties)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+ai-agnisys/lora_model,"---
+base_model: deepseek-ai/deepseek-coder-33b-instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded  model
+
+- **Developed by:** ai-agnisys
+- **License:** apache-2.0
+- **Finetuned from model :** deepseek-ai/deepseek-coder-33b-instruct
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src=""https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png"" width=""200""/>](https://github.com/unslothai/unsloth)
+","{""id"": ""ai-agnisys/lora_model"", ""author"": ""ai-agnisys"", ""sha"": ""ef3123a3c600997ce9404fdb2ac646f0f67c48e3"", ""last_modified"": ""2025-02-21 05:22:20+00:00"", ""created_at"": ""2025-02-21 05:12:18+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""text-generation-inference"", ""unsloth"", ""llama"", ""trl"", ""en"", ""base_model:deepseek-ai/deepseek-coder-33b-instruct"", ""base_model:finetune:deepseek-ai/deepseek-coder-33b-instruct"", ""license:apache-2.0"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: deepseek-ai/deepseek-coder-33b-instruct\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- llama\n- trl"", ""widget_data"": null, ""model_index"": null, ""config"": {""tokenizer_config"": {""bos_token"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""chat_template"": ""{% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% else %}{% for message in messages %}{% if message['from'] == 'human' %}{{'<|im_start|>user\n' + message['value'] + '<|im_end|>\n'}}{% elif message['from'] == 'gpt' %}{{'<|im_start|>assistant\n' + message['value'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['value'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""unk_token"": null, ""use_default_system_prompt"": false}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-02-21 05:22:20+00:00"", ""cardData"": ""base_model: deepseek-ai/deepseek-coder-33b-instruct\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- llama\n- trl"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""67b80b3266410e6ab8d48db9"", ""modelId"": ""ai-agnisys/lora_model"", ""usedStorage"": 492784136}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=ai-agnisys/lora_model&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bai-agnisys%2Flora_model%5D(%2Fai-agnisys%2Flora_model)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+ai-agnisys/ids-integrate-model,"---
+base_model: deepseek-ai/deepseek-coder-33b-instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded  model
+
+- **Developed by:** ai-agnisys
+- **License:** apache-2.0
+- **Finetuned from model :** deepseek-ai/deepseek-coder-33b-instruct
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src=""https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png"" width=""200""/>](https://github.com/unslothai/unsloth)
+","{""id"": ""ai-agnisys/ids-integrate-model"", ""author"": ""ai-agnisys"", ""sha"": ""113da3b775fb22825f102cb754ddd663c82a7913"", ""last_modified"": ""2025-04-08 08:49:43+00:00"", ""created_at"": ""2025-04-08 08:49:25+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""text-generation-inference"", ""unsloth"", ""llama"", ""trl"", ""en"", ""base_model:deepseek-ai/deepseek-coder-33b-instruct"", ""base_model:finetune:deepseek-ai/deepseek-coder-33b-instruct"", ""license:apache-2.0"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: deepseek-ai/deepseek-coder-33b-instruct\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- llama\n- trl"", ""widget_data"": null, ""model_index"": null, ""config"": {""tokenizer_config"": {""bos_token"": ""<\uff5cbegin\u2581of\u2581sentence\uff5c>"", ""chat_template"": ""{% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% else %}{% for message in messages %}{% if message['from'] == 'human' %}{{'<|im_start|>user\n' + message['value'] + '<|im_end|>\n'}}{% elif message['from'] == 'gpt' %}{{'<|im_start|>assistant\n' + message['value'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['value'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% endif %}"", ""eos_token"": ""<|im_end|>"", ""pad_token"": ""<\uff5cend\u2581of\u2581sentence\uff5c>"", ""unk_token"": null, ""use_default_system_prompt"": false}}, ""transformers_info"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-04-08 08:49:43+00:00"", ""cardData"": ""base_model: deepseek-ai/deepseek-coder-33b-instruct\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- llama\n- trl"", ""transformersInfo"": {""auto_model"": ""AutoModel"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": null}, ""_id"": ""67f4e3157d59d3812fd314ed"", ""modelId"": ""ai-agnisys/ids-integrate-model"", ""usedStorage"": 492784136}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=ai-agnisys/ids-integrate-model&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bai-agnisys%2Fids-integrate-model%5D(%2Fai-agnisys%2Fids-integrate-model)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

emotion-english-distilroberta-base_finetunes_20250426_014322.csv_finetunes_20250426_014322.csv

@@ -0,0 +1,292 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+j-hartmann/emotion-english-distilroberta-base,"---
+language: ""en""
+tags:
+- distilroberta
+- sentiment
+- emotion
+- twitter
+- reddit
+
+widget:
+- text: ""Oh wow. I didn't know that.""
+- text: ""This movie always makes me cry..""
+- text: ""Oh Happy Day""
+
+---
+
+# Emotion English DistilRoBERTa-base
+
+# Description ℹ
+
+With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class:
+
+1) anger 🤬
+2) disgust 🤢
+3) fear 😨
+4) joy 😀
+5) neutral 😐
+6) sadness 😭
+7) surprise 😲
+
+The model is a fine-tuned checkpoint of [DistilRoBERTa-base](https://huggingface.co/distilroberta-base). For a 'non-distilled' emotion model, please refer to the model card of the [RoBERTa-large](https://huggingface.co/j-hartmann/emotion-english-roberta-large) version.
+
+# Application 🚀
+
+a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/simple_emotion_pipeline.ipynb)
+
+```python
+from transformers import pipeline
+classifier = pipeline(""text-classification"", model=""j-hartmann/emotion-english-distilroberta-base"", return_all_scores=True)
+classifier(""I love this!"")
+```
+
+```python
+Output:
+[[{'label': 'anger', 'score': 0.004419783595949411},
+  {'label': 'disgust', 'score': 0.0016119900392368436},
+  {'label': 'fear', 'score': 0.0004138521908316761},
+  {'label': 'joy', 'score': 0.9771687984466553},
+  {'label': 'neutral', 'score': 0.005764586851000786},
+  {'label': 'sadness', 'score': 0.002092392183840275},
+  {'label': 'surprise', 'score': 0.008528684265911579}]]
+```
+
+b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/emotion_prediction_example.ipynb)
+
+# Contact 💻
+
+Please reach out to [jochen.hartmann@tum.de](mailto:jochen.hartmann@tum.de) if you have any questions or feedback.
+
+Thanks to Samuel Domdey and [chrsiebert](https://huggingface.co/siebert) for their support in making this model available.
+
+# Reference ✅
+
+For attribution, please cite the following reference if you use this model. A working paper will be available soon.
+
+```
+Jochen Hartmann, ""Emotion English DistilRoBERTa-base"". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022.
+```
+
+BibTex citation:
+
+```
+@misc{hartmann2022emotionenglish,
+  author={Hartmann, Jochen},
+  title={Emotion English DistilRoBERTa-base},
+  year={2022},
+  howpublished = {\url{https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/}},
+}
+```
+
+# Appendix 📚
+
+Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets. The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here. 
+
+|Name|anger|disgust|fear|joy|neutral|sadness|surprise|
+|---|---|---|---|---|---|---|---|
+|Crowdflower (2016)|Yes|-|-|Yes|Yes|Yes|Yes|
+|Emotion Dataset, Elvis et al. (2018)|Yes|-|Yes|Yes|-|Yes|Yes|
+|GoEmotions, Demszky et al. (2020)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
+|ISEAR, Vikash (2018)|Yes|Yes|Yes|Yes|-|Yes|-|
+|MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
+|SemEval-2018, EI-reg, Mohammad et al. (2018) |Yes|-|Yes|Yes|-|Yes|-|
+
+The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). 80% of this balanced subset is used for training and 20% for evaluation. The evaluation accuracy is 66% (vs. the random-chance baseline of 1/7 = 14%).
+
+# Scientific Applications 📖
+
+Below you can find a list of papers using ""Emotion English DistilRoBERTa-base"". If you would like your paper to be added to the list, please send me an email.
+
+- Butt, S., Sharma, S., Sharma, R., Sidorov, G., & Gelbukh, A. (2022). What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses. Computers in Human Behavior, 107345.
+- Kuang, Z., Zong, S., Zhang, J., Chen, J., & Liu, H. (2022). Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings. arXiv preprint arXiv:2210.00434.
+- Rozado, D., Hughes, R., & Halberstadt, J. (2022). Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models. Plos one, 17(10), e0276367.","{""id"": ""j-hartmann/emotion-english-distilroberta-base"", ""author"": ""j-hartmann"", ""sha"": ""0e1cd914e3d46199ed785853e12b57304e04178b"", ""last_modified"": ""2023-01-02 13:03:10+00:00"", ""created_at"": ""2022-03-02 23:29:05+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1093905, ""downloads_all_time"": null, ""likes"": 402, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""pytorch"", ""tf"", ""roberta"", ""text-classification"", ""distilroberta"", ""sentiment"", ""emotion"", ""twitter"", ""reddit"", ""en"", ""arxiv:2210.00434"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-classification"", ""mask_token"": ""<mask>"", ""trending_score"": null, ""card_data"": ""language: en\ntags:\n- distilroberta\n- sentiment\n- emotion\n- twitter\n- reddit\nwidget:\n- text: Oh wow. 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+lunadebruyne/test_trainer,"---
+base_model: j-hartmann/emotion-english-distilroberta-base
+tags:
+- generated_from_trainer
+model-index:
+- name: test_trainer
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# test_trainer
+
+This model is a fine-tuned version of [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Tokenizers 0.15.2
+","{""id"": ""lunadebruyne/test_trainer"", ""author"": ""lunadebruyne"", ""sha"": ""23ca94b95d863acb6b2ffd48ec1e6a7c7e33368a"", ""last_modified"": ""2024-02-21 11:07:29+00:00"", ""created_at"": ""2024-02-21 11:07:14+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 11, ""downloads_all_time"": null, ""likes"": 1, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""tensorboard"", ""safetensors"", ""roberta"", ""text-classification"", ""generated_from_trainer"", ""base_model:j-hartmann/emotion-english-distilroberta-base"", ""base_model:finetune:j-hartmann/emotion-english-distilroberta-base"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-classification"", ""mask_token"": ""<mask>"", ""trending_score"": null, ""card_data"": ""base_model: j-hartmann/emotion-english-distilroberta-base\ntags:\n- generated_from_trainer\nmodel-index:\n- name: test_trainer\n  results: []"", ""widget_data"": [{""text"": ""I like you. I love you""}], ""model_index"": [{""name"": ""test_trainer"", ""results"": []}], ""config"": {""architectures"": [""RobertaForSequenceClassification""], ""model_type"": ""roberta""}, ""transformers_info"": {""auto_model"": ""AutoModelForSequenceClassification"", ""custom_class"": null, ""pipeline_tag"": ""text-classification"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-30-12_385d7f6a99a0/events.out.tfevents.1708511413.385d7f6a99a0.950.0', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-33-10_385d7f6a99a0/events.out.tfevents.1708511591.385d7f6a99a0.950.1', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-34-02_385d7f6a99a0/events.out.tfevents.1708511642.385d7f6a99a0.950.2', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-34-22_385d7f6a99a0/events.out.tfevents.1708511662.385d7f6a99a0.950.3', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-38-01_385d7f6a99a0/events.out.tfevents.1708511882.385d7f6a99a0.950.4', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-38-17_385d7f6a99a0/events.out.tfevents.1708511899.385d7f6a99a0.950.5', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-42-33_385d7f6a99a0/events.out.tfevents.1708512154.385d7f6a99a0.950.6', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-44-52_385d7f6a99a0/events.out.tfevents.1708512293.385d7f6a99a0.950.7', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_10-45-56_385d7f6a99a0/events.out.tfevents.1708512356.385d7f6a99a0.950.8', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_11-00-19_385d7f6a99a0/events.out.tfevents.1708513220.385d7f6a99a0.950.9', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Feb21_11-07-07_385d7f6a99a0/events.out.tfevents.1708513627.385d7f6a99a0.950.10', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""F32"": 82124552}, ""total"": 82124552}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-02-21 11:07:29+00:00"", ""cardData"": ""base_model: j-hartmann/emotion-english-distilroberta-base\ntags:\n- generated_from_trainer\nmodel-index:\n- name: test_trainer\n  results: []"", ""transformersInfo"": {""auto_model"": ""AutoModelForSequenceClassification"", ""custom_class"": null, ""pipeline_tag"": ""text-classification"", ""processor"": ""AutoTokenizer""}, ""_id"": ""65d5d9624f9684c48f4dedea"", ""modelId"": ""lunadebruyne/test_trainer"", ""usedStorage"": 328569723}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=lunadebruyne/test_trainer&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Blunadebruyne%2Ftest_trainer%5D(%2Flunadebruyne%2Ftest_trainer)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+zuriati/results,"---
+library_name: transformers
+base_model: j-hartmann/emotion-english-distilroberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: results
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# results
+
+This model is a fine-tuned version of [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3514
+- Accuracy: 0.9365
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0542        | 1.0   | 1000 | 0.3514          | 0.9365   |
+| 0.0515        | 2.0   | 2000 | 0.3438          | 0.935    |
+| 0.0825        | 3.0   | 3000 | 0.3766          | 0.9315   |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0
+","{""id"": ""zuriati/results"", ""author"": ""zuriati"", ""sha"": ""3da1b66781997b47a143da19c14be48639496c7c"", ""last_modified"": ""2025-03-03 09:43:18+00:00"", ""created_at"": ""2025-03-03 09:41:30+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 1, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""tensorboard"", ""safetensors"", ""roberta"", ""text-classification"", ""generated_from_trainer"", ""base_model:j-hartmann/emotion-english-distilroberta-base"", ""base_model:finetune:j-hartmann/emotion-english-distilroberta-base"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-classification"", ""mask_token"": ""<mask>"", ""trending_score"": null, ""card_data"": ""base_model: j-hartmann/emotion-english-distilroberta-base\nlibrary_name: transformers\nmetrics:\n- accuracy\ntags:\n- generated_from_trainer\nmodel-index:\n- name: results\n  results: []"", ""widget_data"": [{""text"": ""I like you. I love you""}], ""model_index"": [{""name"": ""results"", ""results"": []}], ""config"": {""architectures"": [""RobertaForSequenceClassification""], ""model_type"": ""roberta"", ""tokenizer_config"": {""bos_token"": ""<s>"", ""cls_token"": ""<s>"", ""eos_token"": ""</s>"", ""mask_token"": ""<mask>"", ""pad_token"": ""<pad>"", ""sep_token"": ""</s>"", ""unk_token"": ""<unk>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForSequenceClassification"", ""custom_class"": null, ""pipeline_tag"": ""text-classification"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='merges.txt', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Mar03_07-19-03_b19b2cc4aed0/events.out.tfevents.1740986345.b19b2cc4aed0.5627.0', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='vocab.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""F32"": 82123783}, ""total"": 82123783}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2025-03-03 09:43:18+00:00"", ""cardData"": ""base_model: j-hartmann/emotion-english-distilroberta-base\nlibrary_name: transformers\nmetrics:\n- accuracy\ntags:\n- generated_from_trainer\nmodel-index:\n- name: results\n  results: []"", ""transformersInfo"": {""auto_model"": ""AutoModelForSequenceClassification"", ""custom_class"": null, ""pipeline_tag"": ""text-classification"", ""processor"": ""AutoTokenizer""}, ""_id"": ""67c5794a64747ab8d4504393"", ""modelId"": ""zuriati/results"", ""usedStorage"": 328521007}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=zuriati/results&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bzuriati%2Fresults%5D(%2Fzuriati%2Fresults)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+shengqizhao0124/emotion_trainer,"---
+library_name: transformers
+base_model: j-hartmann/emotion-english-distilroberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: emotion_trainer
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# emotion_trainer
+
+This model is a fine-tuned version of [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3926
+- Accuracy: 0.926
+- Precision: 0.9268
+- Recall: 0.926
+- F1: 0.9257
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.2788        | 1.0   | 625  | 0.3926          | 0.926    | 0.9268    | 0.926  | 0.9257 |
+| 0.04          | 2.0   | 1250 | 0.4845          | 0.922    | 0.9232    | 0.922  | 0.9214 |
+| 0.0834        | 3.0   | 1875 | 0.4531          | 0.93     | 0.9313    | 0.93   | 0.9303 |
+| 0.0364        | 4.0   | 2500 | 0.5419          | 0.9225   | 0.9233    | 0.9225 | 0.9221 |
+| 0.0252        | 5.0   | 3125 | 0.5278          | 0.9265   | 0.9261    | 0.9265 | 0.9260 |
+
+
+### Framework versions
+
+- Transformers 4.50.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.1
+- Tokenizers 0.21.1
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falcon-40b-instruct-GPTQ_finetunes_20250426_215237.csv_finetunes_20250426_215237.csv

@@ -0,0 +1,396 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+TheBloke/falcon-40b-instruct-GPTQ,"---
+datasets:
+- tiiuae/falcon-refinedweb
+license: apache-2.0
+language:
+- en
+inference: false
+---
+
+<!-- header start -->
+<!-- 200823 -->
+<div style=""width: auto; margin-left: auto; margin-right: auto"">
+<img src=""https://i.imgur.com/EBdldam.jpg"" alt=""TheBlokeAI"" style=""width: 100%; min-width: 400px; display: block; margin: auto;"">
+</div>
+<div style=""display: flex; justify-content: space-between; width: 100%;"">
+    <div style=""display: flex; flex-direction: column; align-items: flex-start;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://discord.gg/theblokeai"">Chat & support: TheBloke's Discord server</a></p>
+    </div>
+    <div style=""display: flex; flex-direction: column; align-items: flex-end;"">
+        <p style=""margin-top: 0.5em; margin-bottom: 0em;""><a href=""https://www.patreon.com/TheBlokeAI"">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<div style=""text-align:center; margin-top: 0em; margin-bottom: 0em""><p style=""margin-top: 0.25em; margin-bottom: 0em;"">TheBloke's LLM work is generously supported by a grant from <a href=""https://a16z.com"">andreessen horowitz (a16z)</a></p></div>
+<hr style=""margin-top: 1.0em; margin-bottom: 1.0em;"">
+<!-- header end -->
+
+# Falcon-40B-Instruct 4bit GPTQ
+
+This repo contains an experimantal GPTQ 4bit model for [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct).
+
+It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
+
+## Repositories available
+
+* [4-bit GPTQ model for GPU inference](https://huggingface.co/TheBloke/falcon-40b-instruct-GPTQ)
+* [3-bit GPTQ model for GPU inference](https://huggingface.co/TheBloke/falcon-40b-instruct-3bit-GPTQ)
+* [2, 3, 4, 5, 6, 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/falcon-40b-instruct-GGML)
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/tiiuae/falcon-40b-instruct)
+
+## Prompt template
+
+```
+A helpful assistant who helps the user with any questions asked.
+User: prompt
+Assistant:
+```
+
+## EXPERIMENTAL
+
+Please note this is an experimental GPTQ model. Support for it is currently quite limited.
+
+It is also expected to be **VERY SLOW**. This is currently unavoidable, but is being looked at.
+
+This 4bit model requires at least 35GB VRAM to load. It can be used on 40GB or 48GB cards, but not less.
+
+Please be aware that you should currently expect around 0.7 tokens/s on 40B Falcon GPTQ.
+
+## AutoGPTQ
+
+AutoGPTQ is required: `pip install auto-gptq`
+
+AutoGPTQ provides pre-compiled wheels for Windows and Linux, with CUDA toolkit 11.7 or 11.8.
+
+If you are running CUDA toolkit 12.x, you will need to compile your own by following these instructions:
+
+```
+git clone https://github.com/PanQiWei/AutoGPTQ
+cd AutoGPTQ
+pip install .
+```
+
+These manual steps will require that you have the [Nvidia CUDA toolkit](https://developer.nvidia.com/cuda-12-0-1-download-archive) installed.
+
+## text-generation-webui
+
+There is provisional AutoGPTQ support in text-generation-webui.
+
+This requires text-generation-webui as of commit 204731952ae59d79ea3805a425c73dd171d943c3.
+
+So please first update text-genration-webui to the latest version.
+
+## How to download and use this model in text-generation-webui
+
+1. Launch text-generation-webui
+2. Click the **Model tab**.
+3. Untick **Autoload model**
+4. Under **Download custom model or LoRA**, enter `TheBloke/falcon-40B-instruct-GPTQ`.
+5. Click **Download**.
+6. Wait until it says it's finished downloading.
+7. Click the **Refresh** icon next to **Model** in the top left.
+8. In the **Model drop-down**: choose the model you just downloaded, `falcon-40B-instruct-GPTQ`.
+9. Make sure **Loader** is set to **AutoGPTQ**. This model will not work with ExLlama or GPTQ-for-LLaMa.
+10. Tick **Trust Remote Code**, followed by **Save Settings**
+11. Click **Reload**.
+12. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
+
+## About `trust_remote_code`
+
+Please be aware that this command line argument causes Python code provided by Falcon to be executed on your machine.
+
+This code is required at the moment because Falcon is too new to be supported by Hugging Face transformers. At some point in the future transformers will support the model natively, and then `trust_remote_code` will no longer be needed.
+
+In this repo you can see two `.py` files - these are the files that get executed. They are copied from the base repo at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
+
+## Simple Python example code
+
+To run this code you need to install AutoGPTQ and einops:
+```
+pip install auto-gptq
+pip install einops
+```
+
+You can then run this example code:
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import argparse
+
+model_name_or_path = ""TheBloke/falcon-40b-instruct-GPTQ""
+# You could also download the model locally, and access it there
+# model_name_or_path = ""/path/to/TheBloke_falcon-40b-instruct-GPTQ""
+
+model_basename = ""model""
+
+use_triton = False
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        model_basename=model_basename,
+        use_safetensors=True,
+        trust_remote_code=True,
+        device=""cuda:0"",
+        use_triton=use_triton,
+        quantize_config=None)
+
+prompt = ""Tell me about AI""
+prompt_template=f'''A helpful assistant who helps the user with any questions asked.
+User: {prompt}
+Assistant:''
+
+print(""\n\n*** Generate:"")
+
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+
+# Inference can also be done using transformers' pipeline
+# Note that if you use pipeline, you will see a spurious error message saying the model type is not supported
+# This can be ignored!  Or you can hide it with the following logging line:
+# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
+logging.set_verbosity(logging.CRITICAL)
+
+print(""*** Pipeline:"")
+pipe = pipeline(
+    ""text-generation"",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.95,
+    repetition_penalty=1.15
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
+```
+
+## Provided files
+
+**gptq_model-4bit--1g.safetensors**
+
+This will work with AutoGPTQ 0.2.0 and later.
+
+It was created without groupsize to reduce VRAM requirements, and with `desc_act` (act-order) to improve inference quality.
+
+* `gptq_model-4bit--1g.safetensors`
+  * Works AutoGPTQ 0.2.0 and later.
+    * At this time it does not work with AutoGPTQ Triton, but support will hopefully be added in time.
+  * Works with text-generation-webui using `--trust-remote-code`
+  * Does not work with any version of GPTQ-for-LLaMa
+  * Parameters: Groupsize = None. Act order (desc_act)
+
+<!-- footer start -->
+<!-- 200823 -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Aemon Algiz.
+
+**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
+
+
+Thank you to all my generous patrons and donaters!
+
+And thank you again to a16z for their generous grant.
+
+<!-- footer end -->
+
+# ✨ Original model card: Falcon-40B-Instruct
+
+# ✨ Falcon-40B-Instruct
+
+**Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). It is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt).**
+
+*Paper coming soon 😊.*
+
+## Why use Falcon-40B-Instruct?
+
+* **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).**
+* **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
+
+💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
+
+💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's small brother!
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = ""tiiuae/falcon-40b-instruct""
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    ""text-generation"",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map=""auto"",
+)
+sequences = pipeline(
+   ""Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:"",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f""Result: {seq['generated_text']}"")
+
+```
+
+# Model Card for Falcon-40B-Instruct
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
+- **Model type:** Causal decoder-only;
+- **Language(s) (NLP):** English and French;
+- **License:** [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-7b-instruct/blob/main/LICENSE.txt);
+- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
+
+### Model Source
+
+- **Paper:** *coming soon*.
+
+## Uses
+
+### Direct Use
+
+Falcon-40B-Instruct has been finetuned on a chat dataset.
+
+### Out-of-Scope Use
+
+Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
+
+## Bias, Risks, and Limitations
+
+Falcon-40B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
+
+### Recommendations
+
+We recommend users of Falcon-40B-Instruct to develop guardrails and to take appropriate precautions for any production use.
+
+## How to Get Started with the Model
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = ""tiiuae/falcon-40b-instruct""
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    ""text-generation"",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map=""auto"",
+)
+sequences = pipeline(
+   ""Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:"",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f""Result: {seq['generated_text']}"")
+
+```
+
+## Training Details
+
+### Training Data
+
+Falcon-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data.
+
+
+The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
+
+
+## Evaluation
+
+*Paper coming soon.*
+
+See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
+
+
+## Technical Specifications
+
+For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
+
+### Model Architecture and Objective
+
+Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
+
+The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
+
+* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
+* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
+* **Decoder-block:** parallel attention/MLP with a single layer norm.
+
+For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
+
+| **Hyperparameter** | **Value** | **Comment**                            |
+|--------------------|-----------|----------------------------------------|
+| Layers             | 60        |                                        |
+| `d_model`          | 8192      |                                        |
+| `head_dim`         | 64        | Reduced to optimise for FlashAttention |
+| Vocabulary         | 65024     |                                        |
+| Sequence length    | 2048      |                                        |
+
+### Compute Infrastructure
+
+#### Hardware
+
+Falcon-40B-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances.
+
+#### Software
+
+Falcon-40B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
+
+
+## Citation
+
+*Paper coming soon 😊.*
+
+## License
+
+Falcon-40B-Instruct is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt). Broadly speaking,
+* You can freely use our models for research and/or personal purpose;
+* You are allowed to share and build derivatives of these models, but you are required to give attribution and to share-alike with the same license;
+* For commercial use, you are exempt from royalties payment if the attributable revenues are inferior to $1M/year, otherwise you should enter in a commercial agreement with TII.
+
+
+## Contact
+falconllm@tii.ae
+
+","{""id"": ""TheBloke/falcon-40b-instruct-GPTQ"", ""author"": ""TheBloke"", ""sha"": ""57ac6eae1469d42d37781df19576896490023ec2"", ""last_modified"": ""2023-08-21 11:20:50+00:00"", ""created_at"": ""2023-05-27 09:06:50+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 111, ""downloads_all_time"": null, ""likes"": 197, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""safetensors"", ""RefinedWeb"", ""text-generation"", ""custom_code"", ""en"", ""dataset:tiiuae/falcon-refinedweb"", ""arxiv:2205.14135"", ""arxiv:1911.02150"", ""arxiv:2005.14165"", ""arxiv:2104.09864"", ""license:apache-2.0"", ""autotrain_compatible"", ""text-generation-inference"", ""4-bit"", ""gptq"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""datasets:\n- tiiuae/falcon-refinedweb\nlanguage:\n- en\nlicense: apache-2.0\ninference: false"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""RWForCausalLM""], ""auto_map"": {""AutoConfig"": ""configuration_RW.RWConfig"", ""AutoModelForCausalLM"": ""modelling_RW.RWForCausalLM""}, ""model_type"": ""RefinedWeb"", ""quantization_config"": {""bits"": 4, ""quant_method"": ""gptq""}, ""tokenizer_config"": {""eos_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""modelling_RW.RWForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='configuration_RW.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='modelling_RW.py', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='quantize_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": {""parameters"": {""I32"": 5099957760, ""BF16"": 1067335680, ""F16"": 7004160}, ""total"": 6174297600}, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-08-21 11:20:50+00:00"", ""cardData"": ""datasets:\n- tiiuae/falcon-refinedweb\nlanguage:\n- en\nlicense: apache-2.0\ninference: false"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": ""modelling_RW.RWForCausalLM"", ""pipeline_tag"": ""text-generation"", ""processor"": null}, ""_id"": ""6471c82a97a75cc77aa6cb70"", ""modelId"": ""TheBloke/falcon-40b-instruct-GPTQ"", ""usedStorage"": 45097362760}",0,,0,,0,,0,,0,"HuggingFaceH4/open_llm_leaderboard, huggingface/InferenceSupport/discussions/new?title=TheBloke/falcon-40b-instruct-GPTQ&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BTheBloke%2Ffalcon-40b-instruct-GPTQ%5D(%2FTheBloke%2Ffalcon-40b-instruct-GPTQ)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A",2

flux-lora-collection_finetunes_20250425_143346.csv_finetunes_20250425_143346.csv

@@ -0,0 +1,161 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+XLabs-AI/flux-lora-collection,"---
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.
+language:
+- en
+pipeline_tag: text-to-image
+tags:
+- LoRA
+- Stable Diffusion
+- image-generation
+- Flux
+---
+![FLUX LoRA Collections](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/light/flux-lora-collection-rev1.png?raw=true)
+This repository provides a checkpoint with trained LoRAs for
+[FLUX.1-dev model](https://huggingface.co/black-forest-labs/FLUX.1-dev) by Black Forest Labs
+[<img src=""https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/light/join-our-discord-rev1.png?raw=true"">](https://discord.gg/FHY2guThfy)
+
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/furry4.png?raw=true)
+# ComfyUI
+
+[See our github](https://github.com/XLabs-AI/x-flux-comfyui) for comfy ui workflows.
+![Example Picture 1](https://github.com/XLabs-AI/x-flux-comfyui/blob/main/assets/image1.png?raw=true)
+# Training details
+[XLabs AI](https://github.com/XLabs-AI) team is happy to publish fune-tuning Flux scripts, including:
+
+- **LoRA** 🔥
+- **ControlNet** 🔥
+
+[See our github](https://github.com/XLabs-AI/x-flux) for train script and train configs.
+
+# Training Dataset
+Dataset has the following format for the training process:
+
+```
+├── images/
+│    ├── 1.png
+│    ├── 1.json
+│    ├── 2.png
+│    ├── 2.json
+│    ├── ...
+```
+A .json file contains ""caption"" field with a text prompt.
+
+Thank https://civitai.com/user/dobrosketchkun and https://civitai.com/user/sadxzero for datasets for loras
+
+# Inference
+## furry_lora
+```bash
+python3 main.py \
+ --prompt ""Female furry Pixie with text 'hello world'"" \
+ --lora_repo_id XLabs-AI/flux-furry-lora --lora_name furry_lora.safetensors --device cuda --offload --use_lora \
+ --model_type flux-dev-fp8 --width 1024 --height 1024 \
+ --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5 --guidance 4
+
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/furry4.png?raw=true)
+```bash
+python3 main.py \
+ --prompt ""Male furry Lycanthrope with fur-covered body in ancient ruins, howling at the full moon, surrounded by eerie mist, werewolf transformation, elder scrolls, eslweyr, glitch aesthetic, anime-inspired, digital illustration, artstation, furry"" \
+ --lora_repo_id XLabs-AI/flux-furry-lora --lora_name furry_lora.safetensors --device cuda --offload --use_lora \
+ --model_type flux-dev-fp8 --width 1024 --height 1024 \
+ --timestep_to_start_cfg 1 --num_steps 25 --true_gs 3.5
+
+```
+
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/furry2.png?raw=true)
+## mjv6_lora
+```bash
+python3 main.py \
+--prompt ""A handsome man in a suit, 25 years old, cool, futuristic"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name mjv6_lora.safetensors \
+--device cuda:4 --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_13.png?raw=true)
+
+```bash
+python3 main.py \
+--prompt ""A girl in a suit covered with bold tattoos and holding a vest pistol, beautiful woman, 25 years old, cool, future fantasy, turquoise & light orange ping curl hair"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name mjv6_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_12.png?raw=true)
+## anime_lora
+```bash
+python3 main.py \
+--prompt ""A cute corgi lives in a house made out of sushi, anime"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name anime_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_14.png?raw=true)
+```bash
+python3 main.py \
+--prompt ""a girl with orange hair, standing in a room with a window, looking out at a cityscape, anime"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name anime_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_15.png?raw=true)
+
+## disney_lora
+```bash
+python3 main.py \
+--prompt ""An aerial view of beach with people on it, disney style"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name disney_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_19.png?raw=true)
+
+```bash
+python3 main.py \
+--prompt ""A blue jay standing on a large basket of rainbow macarons, disney style"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name disney_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_18.png?raw=true)
+
+## scenery_lora
+```bash
+python3 main.py \
+--prompt ""A fantasy cityscape with multiple buildings and skyscrapers all of which are covered in snow and ice, scenery style"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name scenery_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_21.png?raw=true)
+
+```bash
+python3 main.py \
+--prompt ""A large ornate building with multiple levels and arches surrounded by trees and greenery. In front of it there are several statues and sculptures on pedestals with fire burning brightly in front of them, scenery style"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name scenery_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_22.png?raw=true)
+## art_lora
+```bash
+python3 main.py \
+--prompt ""white rabbit in blue dress and hat holding bow and arrow, art"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name art_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_23.png?raw=true)
+
+```bash
+python3 main.py \
+--prompt ""castle in the middle of forest at night, art"" \
+--lora_repo_id XLabs-AI/flux-lora-collection --lora_name art_lora.safetensors \
+--device cuda --offload --use_lora --model_type flux-dev-fp8 --width 1024 --height 1024
+```
+![Example Picture 1](https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_24.png?raw=true)
+# License
+
+lora.safetensors falls under the [FLUX.1 [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) Non-Commercial License<br/>","{""id"": ""XLabs-AI/flux-lora-collection"", ""author"": ""XLabs-AI"", ""sha"": ""9ddb42ad970b5d31f50ac02d223adea81413ba4c"", ""last_modified"": ""2024-08-14 15:21:03+00:00"", ""created_at"": ""2024-08-08 16:57:58+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 544, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""LoRA"", ""Stable Diffusion"", ""image-generation"", ""Flux"", ""text-to-image"", ""en"", ""license:other"", ""region:us""], ""pipeline_tag"": ""text-to-image"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""language:\n- en\nlicense: other\nlicense_name: flux-1-dev-non-commercial-license\nlicense_link: 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""khanhere/flux-lora-lab-duplicatedd"", ""reza74ii/flux-lora-the-explorer"", ""jkorstad/flux-lora-the-explorer"", ""soiz1/flux-lora-the-explorer"", ""John6666/flux-lora-the-explorer-test"", ""oscar00oscar/flux-lora-the-explorer"", ""codermert/flux-lora-the-explorer"", ""jheansad/flux-lora-the-explorer""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-08-14 15:21:03+00:00"", ""cardData"": ""language:\n- en\nlicense: other\nlicense_name: flux-1-dev-non-commercial-license\nlicense_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.\npipeline_tag: text-to-image\ntags:\n- LoRA\n- Stable Diffusion\n- image-generation\n- Flux"", ""transformersInfo"": null, ""_id"": ""66b4f9168de19c0bb325177a"", ""modelId"": ""XLabs-AI/flux-lora-collection"", ""usedStorage"": 1479610132}",0,https://huggingface.co/tpinville/lora1,1,"https://huggingface.co/futureaicorner/Flux_1, https://huggingface.co/TRPJ/0, 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multimodalart/flux-lora-lab, multimodalart/flux-lora-the-explorer, seawolf2357/flxloraexp",13
+tpinville/lora1,"---
+license: apache-2.0
+language:
+- en
+base_model:
+- XLabs-AI/flux-lora-collection
+---","{""id"": ""tpinville/lora1"", ""author"": ""tpinville"", ""sha"": ""bdc6b364b1405459a72d7adbccb83217a4aaf607"", ""last_modified"": ""2024-10-14 10:38:16+00:00"", ""created_at"": ""2024-10-14 10:01:02+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""en"", ""base_model:XLabs-AI/flux-lora-collection"", ""base_model:finetune:XLabs-AI/flux-lora-collection"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- XLabs-AI/flux-lora-collection\nlanguage:\n- en\nlicense: apache-2.0"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='stopperlora.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-10-14 10:38:16+00:00"", ""cardData"": ""base_model:\n- XLabs-AI/flux-lora-collection\nlanguage:\n- en\nlicense: apache-2.0"", ""transformersInfo"": null, ""_id"": ""670cebde34108ca89f5faabe"", ""modelId"": ""tpinville/lora1"", ""usedStorage"": 171969424}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=tpinville/lora1&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Btpinville%2Flora1%5D(%2Ftpinville%2Flora1)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1

flux1-dev_finetunes_20250425_165642.csv_finetunes_20250425_165642.csv

@@ -0,0 +1,12 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+Comfy-Org/flux1-dev,"---
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/LICENSE.md
+---
+
+This is a smaller checkpoint for flux1-dev that will work better for ComfyUI users with less VRAM (under 24gb). 
+
+The two text encoders used by Flux are already included in this one safetensor.
+
+Use it with the `Load Checkpoint` node in ComfyUI.","{""id"": ""Comfy-Org/flux1-dev"", ""author"": ""Comfy-Org"", ""sha"": ""f062db3fdcd6a91b73f99236a7dc9cac9f339964"", ""last_modified"": ""2024-09-01 00:37:54+00:00"", ""created_at"": ""2024-08-03 20:04:30+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 506, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""license:other"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""license: other\nlicense_name: flux-1-dev-non-commercial-license\nlicense_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/LICENSE.md"", ""widget_data"": null, ""model_index"": null, ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='flux1-dev-fp8.safetensors', size=None, blob_id=None, lfs=None)""], ""spaces"": [""ramimu/LoRa_Streamlit""], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-09-01 00:37:54+00:00"", ""cardData"": ""license: other\nlicense_name: flux-1-dev-non-commercial-license\nlicense_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/LICENSE.md"", ""transformersInfo"": null, ""_id"": ""66ae8d4edb3168f9d1753958"", ""modelId"": ""Comfy-Org/flux1-dev"", ""usedStorage"": 17246524772}",0,,0,,0,https://huggingface.co/gguf-org/flux-dev-gguf,1,,0,"huggingface/InferenceSupport/discussions/new?title=Comfy-Org/flux1-dev&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BComfy-Org%2Fflux1-dev%5D(%2FComfy-Org%2Fflux1-dev)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A, ramimu/LoRa_Streamlit",2

gpt-neox-20b_finetunes_20250425_143346.csv_finetunes_20250425_143346.csv

@@ -0,0 +1,600 @@
+model_id,card,metadata,depth,children,children_count,adapters,adapters_count,quantized,quantized_count,merges,merges_count,spaces,spaces_count
+EleutherAI/gpt-neox-20b,"---
+language:
+- en
+tags:
+- pytorch
+- causal-lm
+license: apache-2.0
+datasets:
+- EleutherAI/pile
+---
+
+GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained 
+on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX 
+library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally 
+resembles that of GPT-3, and is almost identical to that of [GPT-J-
+6B](https://huggingface.co/EleutherAI/gpt-j-6B). Its training dataset contains 
+a multitude of English-language texts, reflecting the general-purpose nature 
+of this model. See the [accompanying paper](https://arxiv.org/abs/2204.06745) 
+for details about model architecture (including how it differs from GPT-3), 
+training procedure, and additional evaluations.
+
+### Model details
+
+- Developed by: [EleutherAI](http://eleuther.ai)
+- Model type: Transformer-based Language Model
+- Language: English
+- Learn more: [GPT-NeoX-20B: An Open-Source Autoregressive Language 
+Model](https://arxiv.org/abs/2204.06745). For details about the training dataset, 
+see [the Pile paper](https://arxiv.org/abs/2101.00027), and [its data
+sheet](https://arxiv.org/abs/2201.07311).
+- License: Apache 2.0
+- Contact: to ask questions about this model, join the [EleutherAI 
+Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. 
+Please read the existing GPT-NeoX-20B documentation before asking about the model 
+on Discord. For general correspondence: [contact@eleuther.
+ai](mailto:contact@eleuther.ai).
+
+<figure style=""width:30em"">
+
+| Hyperparameter         | Value       |
+| ---------------------- | ----------- |
+| n<sub>parameters</sub> | 20554567680 |
+| n<sub>layers</sub>     | 44          |
+| d<sub>model</sub>      | 6144        |
+| n<sub>heads</sub>      | 64          |
+| d<sub>head</sub>       | 96          |
+| n<sub>vocab</sub>      | 50257       |
+| Sequence Length        | 2048        |
+| Learning Rate          | 0.97 x 10<sup>-5</sup> |
+| Positional Encoding    | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
+</figure>
+
+### Uses and limitations
+
+#### Intended use
+
+GPT-NeoX-20B was developed primarily for research purposes. It learns an inner 
+representation of the English language that can be used to extract features 
+useful for downstream tasks.
+
+In addition to scientific uses, you may also further fine-tune and adapt 
+GPT-NeoX-20B for deployment, as long as your use is in accordance with the 
+Apache 2.0 license. This model works with the [Transformers 
+Library](https://huggingface.co/docs/transformers/index). If you decide to use 
+pre-trained GPT-NeoX-20B as a basis for your fine-tuned model, please note that 
+you need to conduct your own risk and bias assessment. 
+
+#### Out-of-scope use
+
+GPT-NeoX-20B is **not** intended for deployment as-is. It is not a product 
+and cannot be used for human-facing interactions without supervision.
+
+GPT-NeoX-20B has not been fine-tuned for downstream tasks for which language 
+models are commonly deployed, such as writing genre prose, or commercial 
+chatbots. This means GPT-NeoX-20B will likely **not** respond to a given prompt 
+the way products such as ChatGPT do. This is because, unlike GPT-NeoX-20B, 
+ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human 
+Feedback (RLHF) to better “understand” human instructions and dialogue.
+
+This model is English-language only, and thus cannot be used for translation
+or generating text in other languages.
+
+#### Limitations and biases
+
+The core functionality of GPT-NeoX-20B is to take a string of text and predict 
+the next token. Remember that the statistically most likely next token need 
+not result in the most “accurate” text. Never rely on GPT-NeoX-20B to produce 
+factually accurate output.
+
+This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset 
+known to contain profanity and texts that are lewd or otherwise offensive. 
+See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a 
+discussion of documented biases with regards to gender, religion, and race. 
+GPT-NeoX-20B may produce socially unacceptable or undesirable text, *even if*
+ the prompt itself does not include anything explicitly offensive. 
+
+We recommend curating the outputs of this model before presenting it to a human 
+reader. Please inform your audience that you are using artificially generated 
+text. 
+
+#### How to use
+ If you simply want to try out some prompts, check out [this 
+ playground](https://20b.eleuther.ai/).
+ 
+ GPT-NeoX-20B can be loaded using the `AutoModelForCausalLM` functionality:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained(""EleutherAI/gpt-neox-20b"")
+model = AutoModelForCausalLM.from_pretrained(""EleutherAI/gpt-neox-20b"")
+```
+
+### Training
+
+#### Training dataset
+
+The Pile is a 825GiB general-purpose dataset in English. It was created by 
+EleutherAI specifically for training large language models. It contains texts 
+from 22 diverse sources, roughly broken down into five categories: academic 
+writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project 
+Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, 
+Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for 
+a breakdown of all data sources, methodology, and a discussion of ethical 
+implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for 
+more detailed documentation about the Pile and its component datasets. The 
+Pile can be downloaded from the [official website](https://pile.eleuther.ai/), 
+or from a [community mirror](https://the-eye.eu/public/AI/pile/).
+
+The Pile was **not** deduplicated before being used to train GPT-NeoX-20B.
+
+#### Training procedure
+
+GPT-NeoX-20B was trained with a batch size of approximately 3.15M tokens 
+(1538 sequences of 2048 tokens each), for a total of 150,000 steps. Tensor 
+parallelism and pipeline parallelism were used to distribute the model across 
+GPUs. Additional details about the training procedure are in [Section 3 of 
+the accompanying paper](https://arxiv.org/abs/2204.06745).
+
+
+### Evaluations
+
+<figure style=""width:55em"">
+
+| Model         | OpenAI’s LAMBADA | SciQ          | PIQA          | TriviaQA      | ARC (Challenge) |
+| ------------- | :--------------: | :-----------: | :-----------: | :-----------: | :-------------: |
+| GPT-J-6B      | 0.683 ± 0.006    | 0.910 ± 0.009 | 0.752 ± 0.010 | 0.170 ± 0.004 | 0.340 ± 0.014   |
+| FairSeq 6.7B  | 0.673 ± 0.007    | 0.895 ± 0.010 | 0.762 ± 0.010 | 0.221 ± 0.004 | 0.329 ± 0.014   |
+| GPT-3 Curie   | 0.693 ± 0.006    | 0.918 ± 0.009 | 0.767 ± 0.010 | 0.196 ± 0.004 | 0.334 ± 0.014   |
+| FairSeq 13B   | 0.709 ± 0.006    | 0.910 ± 0.009 | 0.769 ± 0.010 | 0.270 ± 0.004 | 0.345 ± 0.014   |
+| GPT-NeoX-20B  | 0.720 ± 0.006    | 0.928 ± 0.008 | 0.779 ± 0.010 | 0.259 ± 0.004 | 0.380 ± 0.014   |
+| GPT-3 DaVinci | 0.752 ± 0.006    | 0.949 ± 0.007 | 0.791 ± 0.009 | 0.409 ± 0.005 | 0.435 ± 0.014   |
+<figcaption>Zero-shot performance on selected natural language tasks.</figcaption>
+</figure>
+
+This is a heavily abridged version of the evaluation results. Appendix D of the
+ [GPT-NeoX-20B paper](https://arxiv.org/abs/2204.06745) compares more model 
+sizes, and contains additional evaluations, including on: zero and five-shot 
+natural language tasks, zero and five-shot Basic Arithmetic and MATH, 
+and zero-shot Hendrycks tasks.
+
+### BibTeX
+
+To cite the GPT-NeoX-20B paper:
+
+```
+@misc{https://doi.org/10.48550/arxiv.2204.06745,
+  doi = {10.48550/ARXIV.2204.06745},
+  
+  url = {https://arxiv.org/abs/2204.06745},
+  
+  author = {Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel},
+  
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {GPT-NeoX-20B: An Open-Source Autoregressive Language Model},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 36.02   |
+| ARC (25-shot)         | 45.73          |
+| HellaSwag (10-shot)   | 73.45    |
+| MMLU (5-shot)         | 25.0         |
+| TruthfulQA (0-shot)   | 31.61   |
+| Winogrande (5-shot)   | 68.9   |
+| GSM8K (5-shot)        | 2.43        |
+| DROP (3-shot)         | 5.04         |
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+shivarama23/outputs,"---
+license: apache-2.0
+base_model: EleutherAI/gpt-neox-20b
+tags:
+- generated_from_trainer
+model-index:
+- name: outputs
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# outputs
+
+This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- training_steps: 20
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.32.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3
+","{""id"": ""shivarama23/outputs"", ""author"": ""shivarama23"", ""sha"": ""92fdd959b735c144e8adb58fae7b8fff389ccbb1"", ""last_modified"": ""2023-07-20 09:05:26+00:00"", ""created_at"": ""2023-07-20 08:59:54+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""generated_from_trainer"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""widget_data"": null, ""model_index"": [{""name"": ""outputs"", ""results"": []}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='.gitignore', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-07-20 09:05:26+00:00"", ""cardData"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""transformersInfo"": null, ""_id"": ""64b8f78af5311dd038284179"", ""modelId"": ""shivarama23/outputs"", ""usedStorage"": 34639476}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=shivarama23/outputs&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bshivarama23%2Foutputs%5D(%2Fshivarama23%2Foutputs)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+solanotodeschini/outputs,"---
+license: apache-2.0
+base_model: EleutherAI/gpt-neox-20b
+tags:
+- generated_from_trainer
+model-index:
+- name: outputs
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# outputs
+
+This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on the None dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- training_steps: 50
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3
+","{""id"": ""solanotodeschini/outputs"", ""author"": ""solanotodeschini"", ""sha"": ""b0e7b98683f8b44eda6d2b2f5c3261808e5b49c8"", ""last_modified"": ""2023-08-31 14:49:52+00:00"", ""created_at"": ""2023-08-31 14:49:49+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""generated_from_trainer"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""widget_data"": null, ""model_index"": [{""name"": ""outputs"", ""results"": []}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-08-31 14:49:52+00:00"", ""cardData"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""transformersInfo"": null, ""_id"": ""64f0a88d31d8a032abbee70f"", ""modelId"": ""solanotodeschini/outputs"", ""usedStorage"": 34639540}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=solanotodeschini/outputs&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bsolanotodeschini%2Foutputs%5D(%2Fsolanotodeschini%2Foutputs)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+paragon-analytics/outputs,"---
+license: apache-2.0
+base_model: EleutherAI/gpt-neox-20b
+tags:
+- generated_from_trainer
+model-index:
+- name: outputs
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# outputs
+
+This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- training_steps: 10
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.34.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.13.3
+","{""id"": ""paragon-analytics/outputs"", ""author"": ""paragon-analytics"", ""sha"": ""d8e9e1da4e83fb240849f05ab57ff92d80ef737c"", ""last_modified"": ""2023-09-13 12:34:14+00:00"", ""created_at"": ""2023-09-13 12:34:12+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""generated_from_trainer"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""widget_data"": null, ""model_index"": [{""name"": ""outputs"", ""results"": []}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-09-13 12:34:14+00:00"", ""cardData"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""transformersInfo"": null, ""_id"": ""6501ac44e55d031001328210"", ""modelId"": ""paragon-analytics/outputs"", ""usedStorage"": 34639540}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=paragon-analytics/outputs&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bparagon-analytics%2Foutputs%5D(%2Fparagon-analytics%2Foutputs)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+vineetsharma/qlora-gpt-neox-20b-english_quotes,"---
+license: apache-2.0
+base_model: EleutherAI/gpt-neox-20b
+tags:
+- generated_from_trainer
+model-index:
+- name: qlora-gpt-neox-20b-english_quotes
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# qlora-gpt-neox-20b-english_quotes
+
+This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- training_steps: 10
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.34.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.0
+","{""id"": ""vineetsharma/qlora-gpt-neox-20b-english_quotes"", ""author"": ""vineetsharma"", ""sha"": ""43a91b283185adbb0d0fd253edce1bf73a3d649e"", ""last_modified"": ""2023-09-30 06:57:25+00:00"", ""created_at"": ""2023-09-30 06:46:10+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""generated_from_trainer"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: qlora-gpt-neox-20b-english_quotes\n  results: []"", ""widget_data"": null, ""model_index"": [{""name"": ""qlora-gpt-neox-20b-english_quotes"", ""results"": []}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-09-30 06:57:25+00:00"", ""cardData"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: qlora-gpt-neox-20b-english_quotes\n  results: []"", ""transformersInfo"": null, ""_id"": ""6517c432a1a5e5d6178ba519"", ""modelId"": ""vineetsharma/qlora-gpt-neox-20b-english_quotes"", ""usedStorage"": 69279208}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=vineetsharma/qlora-gpt-neox-20b-english_quotes&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5Bvineetsharma%2Fqlora-gpt-neox-20b-english_quotes%5D(%2Fvineetsharma%2Fqlora-gpt-neox-20b-english_quotes)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+Electricarchmage/outputs,"---
+license: apache-2.0
+base_model: EleutherAI/gpt-neox-20b
+tags:
+- generated_from_trainer
+model-index:
+- name: outputs
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# outputs
+
+This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- training_steps: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.36.0.dev0
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0
+","{""id"": ""Electricarchmage/outputs"", ""author"": ""Electricarchmage"", ""sha"": ""c896238d46f2f012e0bb3068296933268653f8e8"", ""last_modified"": ""2023-12-02 06:08:18+00:00"", ""created_at"": ""2023-12-02 06:01:49+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": null, ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""tensorboard"", ""safetensors"", ""generated_from_trainer"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""region:us""], ""pipeline_tag"": null, ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""widget_data"": null, ""model_index"": [{""name"": ""outputs"", ""results"": []}], ""config"": null, ""transformers_info"": null, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='adapter_model.safetensors', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='runs/Dec02_05-49-59_2be63012f009/events.out.tfevents.1701496202.2be63012f009.246.0', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='training_args.bin', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2023-12-02 06:08:18+00:00"", ""cardData"": ""base_model: EleutherAI/gpt-neox-20b\nlicense: apache-2.0\ntags:\n- generated_from_trainer\nmodel-index:\n- name: outputs\n  results: []"", ""transformersInfo"": null, ""_id"": ""656ac84d271c5c4e33ca66dd"", ""modelId"": ""Electricarchmage/outputs"", ""usedStorage"": 34627242}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=Electricarchmage/outputs&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BElectricarchmage%2Foutputs%5D(%2FElectricarchmage%2Foutputs)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+OpenVINO/gpt-neox-20b-fp16-ov,"---
+license: apache-2.0
+license_link: https://choosealicense.com/licenses/apache-2.0/
+base_model:
+- EleutherAI/gpt-neox-20b
+---
+# gpt-neox-20b-fp16-ov
+* Model creator: [EleutherAI](https://huggingface.co/EleutherAI)
+ * Original model: [gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b)
+
+## Description
+
+This is [gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format.
+
+## Compatibility
+
+The provided OpenVINO™ IR model is compatible with:
+
+* OpenVINO version 2024.2.0 and higher
+* Optimum Intel 1.19.0 and higher
+
+# Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
+
+<!-- Example model usage -->
+
+1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
+
+```
+pip install optimum[openvino]
+```
+
+2. Run model inference:
+
+<!-- Usage example can be adopted from original model usage example -->
+
+```
+from transformers import AutoTokenizer
+from optimum.intel.openvino import OVModelForCausalLM
+
+model_id = ""OpenVINO/gpt-neox-20b-fp16-ov""
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id)
+
+inputs = tokenizer(""What is OpenVINO?"", return_tensors=""pt"")
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
+For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
+
+<!-- Usage example with OpenVINO GenAI if applicable -->
+## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
+
+1. Install packages required for using OpenVINO GenAI.
+```
+pip install openvino-genai huggingface_hub
+```
+
+2. Download model from HuggingFace Hub
+   
+```
+import huggingface_hub as hf_hub
+
+model_id = ""OpenVINO/gpt-neox-20b-fp16-ov""
+model_path = ""gpt-neox-20b-fp16-ov""
+
+hf_hub.snapshot_download(model_id, local_dir=model_path)
+
+```
+
+3. Run model inference:
+
+```
+import openvino_genai as ov_genai
+
+device = ""CPU""
+pipe = ov_genai.LLMPipeline(model_path, device)
+print(pipe.generate(""What is OpenVINO?"", max_length=200))
+```
+
+More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
+
+## Limitations
+
+Check the original model card for [limitations](https://huggingface.co/EleutherAI/gpt-neox-20b#limitations-and-biases).
+
+## Legal information
+
+The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/EleutherAI/gpt-neox-20b).
+
+## Disclaimer
+
+Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.","{""id"": ""OpenVINO/gpt-neox-20b-fp16-ov"", ""author"": ""OpenVINO"", ""sha"": ""26f59f41ad3349558584cfc188b20e0b428e9db8"", ""last_modified"": ""2024-11-05 10:05:15+00:00"", ""created_at"": ""2024-06-21 06:39:19+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 2, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""openvino"", ""gpt_neox"", ""text-generation"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""license:apache-2.0"", ""autotrain_compatible"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- EleutherAI/gpt-neox-20b\nlicense: apache-2.0\nlicense_link: https://choosealicense.com/licenses/apache-2.0/"", ""widget_data"": [{""text"": ""My name is Julien and I like to""}, {""text"": ""I like traveling by train because""}, {""text"": ""Paris is an amazing place to visit,""}, {""text"": ""Once upon a time,""}], ""model_index"": null, ""config"": {""architectures"": [""GPTNeoXForCausalLM""], ""model_type"": ""gpt_neox"", ""tokenizer_config"": {""bos_token"": ""<|endoftext|>"", ""eos_token"": ""<|endoftext|>"", ""pad_token"": null, ""unk_token"": ""<|endoftext|>""}}, ""transformers_info"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='generation_config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_detokenizer.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_detokenizer.xml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_model.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_model.xml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_tokenizer.bin', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='openvino_tokenizer.xml', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='special_tokens_map.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='tokenizer_config.json', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-11-05 10:05:15+00:00"", ""cardData"": ""base_model:\n- EleutherAI/gpt-neox-20b\nlicense: apache-2.0\nlicense_link: https://choosealicense.com/licenses/apache-2.0/"", ""transformersInfo"": {""auto_model"": ""AutoModelForCausalLM"", ""custom_class"": null, ""pipeline_tag"": ""text-generation"", ""processor"": ""AutoTokenizer""}, ""_id"": ""66752017dbe75ba7861c77e3"", ""modelId"": ""OpenVINO/gpt-neox-20b-fp16-ov"", ""usedStorage"": 41114735255}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=OpenVINO/gpt-neox-20b-fp16-ov&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BOpenVINO%2Fgpt-neox-20b-fp16-ov%5D(%2FOpenVINO%2Fgpt-neox-20b-fp16-ov)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+Mahler60/Prueba,"---
+language:
+- es
+- en
+base_model:
+- EleutherAI/gpt-neox-20b
+tags:
+- neox
+- chatbot
+- español
+metrics:
+- bleu
+- rouge
+- bertscore
+pipeline_tag: text-generation
+library_name: transformers
+---","{""id"": ""Mahler60/Prueba"", ""author"": ""Mahler60"", ""sha"": ""85f42071a4b6b8e4e3c19101dea2b9d0c72c2a93"", ""last_modified"": ""2024-11-27 15:45:38+00:00"", ""created_at"": ""2024-11-26 17:32:35+00:00"", ""private"": false, ""gated"": false, ""disabled"": false, ""downloads"": 0, ""downloads_all_time"": null, ""likes"": 0, ""library_name"": ""transformers"", ""gguf"": null, ""inference"": null, ""inference_provider_mapping"": null, ""tags"": [""transformers"", ""gpt_neox"", ""neox"", ""chatbot"", ""espa\u00f1ol"", ""text-generation"", ""es"", ""en"", ""base_model:EleutherAI/gpt-neox-20b"", ""base_model:finetune:EleutherAI/gpt-neox-20b"", ""endpoints_compatible"", ""region:us""], ""pipeline_tag"": ""text-generation"", ""mask_token"": null, ""trending_score"": null, ""card_data"": ""base_model:\n- EleutherAI/gpt-neox-20b\nlanguage:\n- es\n- en\nlibrary_name: transformers\nmetrics:\n- bleu\n- rouge\n- bertscore\npipeline_tag: text-generation\ntags:\n- neox\n- chatbot\n- espa\u00f1ol"", ""widget_data"": [{""text"": ""Me llamo Julien y me gusta""}, {""text"": ""Me llamo Thomas y mi principal""}, {""text"": ""Me llamo Manuel y trabajo en""}, {""text"": ""\u00c9rase una vez,""}, {""text"": ""Si t\u00fa me dices ven, ""}], ""model_index"": null, ""config"": {""architectures"": [""Mahler60/Prueba""], ""model_type"": ""gpt_neox""}, ""transformers_info"": {""auto_model"": ""Mahler60/Prueba"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": ""AutoTokenizer""}, ""siblings"": [""RepoSibling(rfilename='.gitattributes', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='README.md', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='config.json', size=None, blob_id=None, lfs=None)"", ""RepoSibling(rfilename='fine_tunnig_yuuka.py.txt', size=None, blob_id=None, lfs=None)""], ""spaces"": [], ""safetensors"": null, ""security_repo_status"": null, ""xet_enabled"": null, ""lastModified"": ""2024-11-27 15:45:38+00:00"", ""cardData"": ""base_model:\n- EleutherAI/gpt-neox-20b\nlanguage:\n- es\n- en\nlibrary_name: transformers\nmetrics:\n- bleu\n- rouge\n- bertscore\npipeline_tag: text-generation\ntags:\n- neox\n- chatbot\n- espa\u00f1ol"", ""transformersInfo"": {""auto_model"": ""Mahler60/Prueba"", ""custom_class"": null, ""pipeline_tag"": null, ""processor"": ""AutoTokenizer""}, ""_id"": ""6746063308678f45e12b9d75"", ""modelId"": ""Mahler60/Prueba"", ""usedStorage"": 0}",1,,0,,0,,0,,0,huggingface/InferenceSupport/discussions/new?title=Mahler60/Prueba&description=React%20to%20this%20comment%20with%20an%20emoji%20to%20vote%20for%20%5BMahler60%2FPrueba%5D(%2FMahler60%2FPrueba)%20to%20be%20supported%20by%20Inference%20Providers.%0A%0A(optional)%20Which%20providers%20are%20you%20interested%20in%3F%20(Novita%2C%20Hyperbolic%2C%20Together%E2%80%A6)%0A,1
+ChatBotExploit/NexusV3,"---
+base_model:
+- EleutherAI/gpt-neox-20b
+tags:
+- code
+datasets:
+- Roblox/luau_corpus
+library_name: flair
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