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text-generation | transformers |
<!-- 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. -->
# HPY_gpt2_v2
This model is a fine-tuned version of [ClassCat/gpt2-base-french](https://huggingface.co/ClassCat/gpt2-base-french) ... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "HPY_gpt2_v2", "results": []}]} | azizkt/HPY_gpt2_v2 | null | [
"transformers",
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"tensorboard",
"gpt2",
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"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| HPY\_gpt2\_v2
=============
This model is a fine-tuned version of ClassCat/gpt2-base-french on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1060
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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text-generation | transformers | Quantizations of https://huggingface.co/icefog72/WestIceLemonTeaRP-32k-7b
# From original readme
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
Prompt template: Alpaca, maybe ChatML
* measurement.json for quanting exl2 included.
- [4.2b... | {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "WestIceLemonTeaRP-32k-7b", "icefog72"], "inference": false, "pipeline_tag": "text-generation"} | duyntnet/WestIceLemonTeaRP-32k-7b-imatrix-GGUF | null | [
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#transformers #gguf #imatrix #WestIceLemonTeaRP-32k-7b #icefog72 #text-generation #en #license-other #region-us
| Quantizations of URL
From original readme
====================
This is a merge of pre-trained language models created using mergekit.
Merge Details
-------------
Prompt template: Alpaca, maybe ChatML
* URL for quanting exl2 included.
* 4.2bpw-exl2
* 6.5bpw-exl2
* 8bpw-exl2
thx mradermacher and SilverFan f... | [
"### Merge Method\n\n\nThis model was merged using the SLERP merge method.",
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text-generation | null |
# Phi 3 Mini 4K Instruct GGUF
**Original model**: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
**Model creator**: [Microsoft](https://huggingface.co/microsoft)
This repo contains GGUF format model files for Microsoft’s Phi 3 Mini 4K Instruct.
> The Phi-3-Mini-4K-Instruct is a 3... | {"language": ["en"], "license": "mit", "tags": ["nlp", "code"], "model_name": "Phi-3-mini-4k-instruct", "base_model": "microsoft/Phi-3-mini-4k-instruct", "inference": false, "license_link": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "model_creator"... | brittlewis12/Phi-3-mini-4k-instruct-GGUF | null | [
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"license:mit",
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"en"
] | TAGS
#gguf #nlp #code #text-generation #en #base_model-microsoft/Phi-3-mini-4k-instruct #license-mit #region-us
| Phi 3 Mini 4K Instruct GGUF
===========================
Original model: Phi-3-mini-4k-instruct
Model creator: Microsoft
This repo contains GGUF format model files for Microsoft’s Phi 3 Mini 4K Instruct.
>
> The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with ... | [
"### What is GGUF?\n\n\nGGUF is a file format for representing AI models. It is the third version of the format,\nintroduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nConverted with URL build 2721 (revision 28103f4),\nusing autogguf.",
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image-to-image | diffusers |
# BRIA 2.3 ControlNet ColorGrid Model Card
BRIA 2.3 ControlNet-ColorGrid, trained on the foundation of [BRIA 2.3 Text-to-Image](https://huggingface.co/briaai/BRIA-2.3), enables the generation of high-quality images guided by a textual prompt and the extracted color grid from the input image. This allows for the crea... | {"license": "other", "tags": ["text-to-image", "controlnet model", "legal liability", "commercial use"], "license_name": "bria-2.3", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "pipeline_tag": "image-to-image", "inference": false, "extra_gated_prompt": "This model weights by BRIA AI can... | briaai/BRIA-2.3-ControlNet-ColorGrid | null | [
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"legal liability",
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"image-to-image",
"license:other",
"region:us"
] | null | 2024-04-24T13:59:14+00:00 | [] | [] | TAGS
#diffusers #text-to-image #controlnet model #legal liability #commercial use #image-to-image #license-other #region-us
|
# BRIA 2.3 ControlNet ColorGrid Model Card
BRIA 2.3 ControlNet-ColorGrid, trained on the foundation of BRIA 2.3 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted color grid from the input image. This allows for the creation of different scenes, all sharing the ... | [
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HirCoir/MiniChat-1.5-3B-Sorah
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not sh... | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["unsloth", "trl", "sft"], "base_model": "HirCoir/MiniChat-1.5-3B-Sorah", "quantized_by": "mradermacher"} | mradermacher/MiniChat-1.5-3B-Sorah-GGUF | null | [
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"endpoints_compatible",
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"en"
] | TAGS
#transformers #gguf #unsloth #trl #sft #en #base_model-HirCoir/MiniChat-1.5-3B-Sorah #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF file... | [] | [
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] |
text-generation | transformers |
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mis... | {"license": "apache-2.0", "inference": false} | TitanML/llava-v1.6-mistral-7b | null | [
"transformers",
"safetensors",
"llava",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2024-04-24T14:01:48+00:00 | [] | [] | TAGS
#transformers #safetensors #llava #text-generation #conversational #license-apache-2.0 #autotrain_compatible #region-us
|
<br>
<br>
# LLaVA Model Card
## Model details
Model type:
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: mistralai/Mistral-7B-Instruct-v0.2
Model date:
LLaVA-v1.6-Mistral-... | [
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"## Model details\n\nModel type:\nLLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\nBase LLM: mistralai/Mistral-7B-Instruct-v0.2\n\nModel date:\nLLaVA-v1.6-Mis... | [
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reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hossniper/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- ... | hossniper/Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-24T14:02:03+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
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] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit... | {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | MY11111111/ppo-SnowballTarget | null | [
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"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null | 2024-04-24T14:04:06+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tuto... | [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *... | [
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text-generation | transformers |
<br/><br/>
Testing...
8bpw/h8 exl2 quantization of [xxx777xxxASD/ChaoticSoliloquy-4x8B](https://huggingface.co/xxx777xxxASD/ChaoticSoliloquy-4x8B) using [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) calibration dataset (l=8192, r=200).
---
**ORIGINAL CARD:**
![image/png](https://cdn-uploads.huggi... | {"language": ["en"], "license": "llama3", "tags": ["moe"]} | JayhC/ChaoticSoliloquy-4x8B-8bpw-h8-exl2-rpcal | null | [
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"mixtral",
"text-generation",
"moe",
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"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-24T14:05:14+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
|
<br/><br/>
Testing...
8bpw/h8 exl2 quantization of xxx777xxxASD/ChaoticSoliloquy-4x8B using PIPPA calibration dataset (l=8192, r=200).
---
ORIGINAL CARD:
!image/png
(Maybe i'll change the waifu picture later)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtr... | [
"### ChaoticSoliloquy-4x8B",
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"## Vision\n\nllama3_mmproj\n!image/png",
"## Prompt format: Llama 3"
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"### ChaoticSoliloquy-4x8B",
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translation | transformers |
<!-- 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. -->
# marian-finetuned-kde4-en-to-cn
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "base_model": "Helsinki-NLP/opus-mt-en-zh", "model-index": [{"name": "marian-finetuned-kde4-en-to-cn", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Mode... | zhenchuan/marian-finetuned-kde4-en-to-cn | null | [
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"marian",
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-24T14:06:12+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-zh #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-cn
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9332
- Bleu: 40.6073
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
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video-classification | transformers |
<!-- 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. -->
# videomae-base-finetuned-isl-numbers-alphabet-nouns
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://hugging... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-isl-numbers-alphabet-nouns", "results": []}]} | latif98/videomae-base-finetuned-isl-numbers-alphabet-nouns | null | [
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"license:cc-by-nc-4.0",
"endpoints_compatible",
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| videomae-base-finetuned-isl-numbers-alphabet-nouns
==================================================
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4278
* Accuracy: 0.8875
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
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text-classification | transformers | 'eval_accuracy': 0.68516, 'eval_f1': 0.6844490693226439, 'eval_precision': 0.6839923350377614, 'eval_recall': 0.68516 | {} | TungLe7661/BERT650 | null | [
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WesPro/PsykidelicLlama3
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up ... | {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "WesPro/PsykidelicLlama3", "quantized_by": "mradermacher"} | mradermacher/PsykidelicLlama3-GGUF | null | [
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"en"
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#transformers #gguf #mergekit #merge #en #base_model-WesPro/PsykidelicLlama3 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF file... | [] | [
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text-generation | null |
# noeljacob/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [or... | {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Dat... | NoelJacob/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF | null | [
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|
# noeljacob/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from 'meta-llama/Meta-Llama-3-8B-Instruct' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
... | [
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text-generation | transformers |
# Uploaded model
- **Developed by:** gutsartifical
- **License:** apache-2.0
- **Finetuned from model :** NousResearch/Hermes-2-Pro-Mistral-7B
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/un... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "NousResearch/Hermes-2-Pro-Mistral-7B"} | gutsartificial/hermes-2-pro-entity-cleaning | null | [
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|
# Uploaded model
- Developed by: gutsartifical
- License: apache-2.0
- Finetuned from model : NousResearch/Hermes-2-Pro-Mistral-7B
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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null | peft |
<!-- 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. -->
# HSE_PRAVO_complexity_classifier_large
This model is a fine-tuned version of [ai-forever/ruBert-large](https://huggingface.co/ai-... | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-large", "model-index": [{"name": "HSE_PRAVO_complexity_classifier_large", "results": []}]} | marcus2000/HSE_PRAVO_complexity_classifier_large | null | [
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#peft #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-large #region-us
|
# HSE_PRAVO_complexity_classifier_large
This model is a fine-tuned version of ai-forever/ruBert-large on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
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text-classification | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | kangXn/enta-sb-mde | null | [
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|
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text2text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | IbrahimSalah/Quran_syll_to_word | null | [
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question-answering | transformers |
<!-- 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. -->
# Evidence_Retrieval_model_vi_mrc
This model is a fine-tuned version of [nguyenvulebinh/vi-mrc-base](https://huggingface.co/nguyen... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "base_model": "nguyenvulebinh/vi-mrc-base", "model-index": [{"name": "Evidence_Retrieval_model_vi_mrc", "results": []}]} | tringuyen-uit/Evidence_Retrieval_model_vi_mrc | null | [
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| Evidence\_Retrieval\_model\_vi\_mrc
===================================
This model is a fine-tuned version of nguyenvulebinh/vi-mrc-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4764
Model description
-----------------
More information needed
Intended uses & limi... | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": ["trl", "sft"]} | nicolarsen/LLama3-8B-V1 | null | [
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text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [arcee-ai/sec-mistral-7b-instruct-1.6-epoch]... | {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["arcee-ai/sec-mistral-7b-instruct-1.6-epoch", "cognitivecomputations/dolphin-2.8-mistral-7b-v02"]} | llm-wizard/NousWizard | null | [
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"text-generation-inference... | null | 2024-04-24T14:19:31+00:00 | [] | [] | TAGS
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| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* arcee-ai/sec-mistral-7b-instruct-1.6-epoch
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text-generation | transformers |
# LewdPlay-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The new EVOLVE merge method was used (on MMLU specifically), see below for more information!
Unholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for t... | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["vicgalle/Roleplay-Llama-3-8B", "Undi95/Llama-3-Unholy-8B-e4", "Undi95/Llama-3-LewdPlay-8B"]} | Undi95/Llama-3-LewdPlay-8B-evo | null | [
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# LewdPlay-8B
This is a merge of pre-trained language models created using mergekit.
The new EVOLVE merge method was used (on MMLU specifically), see below for more information!
Unholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for the... lewd side.
## Prompt template:... | [
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null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_... | {"library_name": "peft"} | Rimyy/TentativeLlamaGsm1ep | null | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_38 | null | [
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|
# Model Card for Model ID
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null | peft |
<!-- 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. -->
# RM-HH-Gemma_helpful_human_loraR64_20000_gemma2b_shuffleTrue_extractchosenFalse
This model is a fine-tuned version of [google/gem... | {"license": "gemma", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/gemma-2b", "model-index": [{"name": "RM-HH-Gemma_helpful_human_loraR64_20000_gemma2b_shuffleTrue_extractchosenFalse", "results": []}]} | Holarissun/RM-HH-Gemma_helpful_human_loraR64_20000_gemma2b_shuffleTrue_extractchosenFalse | null | [
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=====================================================================================
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text-generation | transformers |
# Opus-Samantha-Llama-3-8B
Opus-Samantha-Llama-3-8B is a SFT model made with [AutoSloth](https://colab.research.google.com/drive/1Zo0sVEb2lqdsUm9dy2PTzGySxdF9CNkc#scrollTo=MmLkhAjzYyJ4) by [macadeliccc](https://huggingface.co/macadeliccc)
Trained on 1xL4 for 1 hour
_model is curretly very nsfw. uneven distribution ... | {"license": "apache-2.0", "datasets": ["macadeliccc/opus_samantha"]} | macadeliccc/Opus-Samantha-Llama-3-8B | null | [
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|
# Opus-Samantha-Llama-3-8B
Opus-Samantha-Llama-3-8B is a SFT model made with AutoSloth by macadeliccc
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null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | cesaenv/futurama | null | [
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null | transformers |
# Uploaded model
- **Developed by:** nicolarsen
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
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/unslot... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | nicolarsen/LLama3-8B-Meoo | null | [
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null | peft |
<!-- 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. -->
# llava-1.5-7b-hf-med
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7... | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "llava-1.5-7b-hf-med", "results": []}]} | hari02/llava-1.5-7b-hf-med | null | [
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#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-llava-hf/llava-1.5-7b-hf #region-us
|
# llava-1.5-7b-hf-med
This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf 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 hyperparame... | [
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"## Intended uses & limitations\n\nMore information needed",
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text-generation | transformers |
# Medical-Llama3-8B-4bit: Fine-Tuned Llama3 for Medical Q&A
[](https://ruslanmv.com/)
Medical fine tuned version of LLAMA-3-8B quantized in 4 bits using common open source datasets and showing improvements over multilingual tasks. It has been used the standard bitquantized technique for post-fine-tuni... | {"language": "en", "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "ruslanmv", "llama", "trl"], "datasets": ["ruslanmv/ai-medical-chatbot"], "base_model": "meta-llama/Meta-Llama-3-8B"} | ruslanmv/llama3-8B-medical | null | [
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|
# Medical-Llama3-8B-4bit: Fine-Tuned Llama3 for Medical Q&A
![](URL
Medical fine tuned version of LLAMA-3-8B quantized in 4 bits using common open source datasets and showing improvements over multilingual tasks. It has been used the standard bitquantized technique for post-fine-tuning quantization reducing the comp... | [
"# Medical-Llama3-8B-4bit: Fine-Tuned Llama3 for Medical Q&A\n![](URL\n\n\nMedical fine tuned version of LLAMA-3-8B quantized in 4 bits using common open source datasets and showing improvements over multilingual tasks. It has been used the standard bitquantized technique for post-fine-tuning quantization reducing ... | [
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null | peft |
<!-- 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. -->
# Yi-6B-zhihu7
This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the zhihu dataset.
It ac... | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["zhihu"], "base_model": "01-ai/Yi-6B", "model-index": [{"name": "Yi-6B-zhihu7", "results": []}]} | yyx123/Yi-6B-zhihu7 | null | [
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| Yi-6B-zhihu7
============
This model is a fine-tuned version of 01-ai/Yi-6B on the zhihu dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5970
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
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null | peft |
<!-- 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. -->
# leagaleasy-llama-3-instruct-v2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.c... | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "leagaleasy-llama-3-instruct-v2", "results": []}]} | llm-wizard/leagaleasy-llama-3-instruct-v2 | null | [
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|
# leagaleasy-llama-3-instruct-v2
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generate... | {"library_name": "transformers", "tags": []} | Lakoc/voxpopuli_unigram100_cz | null | [
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null | transformers |
# Uploaded model
- **Developed by:** hanifsyarubany10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/un... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | hanifsyarubany10/gemma-7b-50epochs-Unsloth-LaMini-1e-3 | null | [
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null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generate... | {"library_name": "transformers", "tags": []} | Lakoc/voxpopuli_unigram_50_cz | null | [
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null | null |
# A Llama 3 model fine tuned to use Fauna Query Language (FQL)
### What is Fauna?
Fauna is a relational database with a document data model built for modern applications.
Learn more [here](https://docs.fauna.com/fauna/current/what_is_fauna)
This LLAMA 3 model is fine tuned to use [FQL](https://docs.fauna.com/fauna/c... | {"license": "mit", "author": "Shadid Haque"} | Shadid/fauna-lora | null | [
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# A Llama 3 model fine tuned to use Fauna Query Language (FQL)
### What is Fauna?
Fauna is a relational database with a document data model built for modern applications.
Learn more here
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | ai-maker-space/leagaleasy-llama-3-instruct-v2 | null | [
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null | transformers |
# Uploaded model
- **Developed by:** CarolLiu999
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | CarolLiu999/unsloth-mistral-7b-instruct-v0.2-bnb-4bit-lora-TWhealthCare | null | [
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|
# Uploaded model
- Developed by: CarolLiu999
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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text-generation | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_pat... | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | himanshu0410/chatfolio | null | [
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"region:us"
] | null | 2024-04-24T14:33:41+00:00 | [] | [] | TAGS
#transformers #safetensors #autotrain #text-generation-inference #text-generation #peft #conversational #license-other #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
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] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/norygano/Llama-3-TRACHI-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show ... | {"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "norygano/Llama-3-TRACHI-8B", "quantized_by": "mradermacher"} | mradermacher/Llama-3-TRACHI-8B-GGUF | null | [
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"base_model:norygano/Llama-3-TRACHI-8B",
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] | null | 2024-04-24T14:36:49+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-norygano/Llama-3-TRACHI-8B #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF file... | [] | [
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summarization | transformers |
<!-- 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. -->
# t5-small-finetuned-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "pipeline_tag": "summarization", "model-index": [{"name": "t5-small-finetuned-samsum", "results": []}]} | Vexemous/t5-small-finetuned-samsum | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-24T14:38:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-samsum
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7651
* Rouge1: 41.6124
* Rouge2: 18.7668
* Rougel: 35.0271
* Rougelsum: 38.5305
* Gen Len: 16.6381
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
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null | transformers |
# Uploaded model
- **Developed by:** hanifsyarubany10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/un... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | hanifsyarubany10/gemma-7b-50epochs-Unsloth-LaMini-2e-4 | null | [
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
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"en"
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#transformers #safetensors #text-generation-inference #unsloth #gemma #trl #en #base_model-unsloth/gemma-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: hanifsyarubany10
- License: apache-2.0
- Finetuned from model : unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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null | transformers |
# Uploaded model
- **Developed by:** macadeliccc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
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/i... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b"} | macadeliccc/Opus-Samantha-Llama-3-8B-GGUF | null | [
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#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/llama-3-8b #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: macadeliccc
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
... | {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | FrancescoArno94/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-04-24T14:40:27+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you te... | [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* wh... | [
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35,
201
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"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrot... |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'Lunar... | {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2... | SparkleDark/PPO_cart | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | null | 2024-04-24T14:41:21+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
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] |
audio-classification | transformers |
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name":... | MacByner/distilhubert-finetuned-gtzan | null | [
"transformers",
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"dataset:marsyas/gtzan",
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-24T14:41:35+00:00 | [] | [] | TAGS
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| <img src="URL alt="Visualize in Weights & Biases" width="200" height="32"/>
distilhubert-finetuned-gtzan
============================
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0125
* Accuracy: 0.73
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
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null | transformers |
# umarigan/LLama-3-8B-Instruction-tr-Q4_K_M-GGUF
This model was converted to GGUF format from [`umarigan/LLama-3-8B-Instruction-tr`](https://huggingface.co/umarigan/LLama-3-8B-Instruction-tr) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [orig... | {"language": ["en", "tr"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft", "llama-cpp", "gguf-my-repo"], "datasets": ["umarigan/GPTeacher-General-Instruct-tr"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | umarigan/LLama-3-8B-Instruction-tr-Q4_K_M-GGUF | null | [
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"tr",
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"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-24T14:41:48+00:00 | [] | [
"en",
"tr"
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#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #llama-cpp #gguf-my-repo #en #tr #dataset-umarigan/GPTeacher-General-Instruct-tr #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# umarigan/LLama-3-8B-Instruction-tr-Q4_K_M-GGUF
This model was converted to GGUF format from 'umarigan/LLama-3-8B-Instruction-tr' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
... | [
"# umarigan/LLama-3-8B-Instruction-tr-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'umarigan/LLama-3-8B-Instruction-tr' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL serv... | [
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text-generation | null |
# CodeQwen1.5-7B-Chat - SOTA GGUF
- Model creator: [Qwen](https://huggingface.co/Qwen)
- Original model: [CodeQwen1.5-7B-Chat](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat)
<!-- description start -->
## Description
This repo contains State Of The Art quantized GGUF format model files for [CodeQwen1.5-7B-Chat](htt... | {"language": ["en"], "license": "other", "tags": ["chat"], "datasets": ["m-a-p/CodeFeedback-Filtered-Instruction"], "model_name": "CodeQwen1.5-7B-Chat", "base_model": "Qwen/CodeQwen1.5-7B-Chat", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE", "pipel... | CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF | null | [
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"text-generation",
"en",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"base_model:Qwen/CodeQwen1.5-7B-Chat",
"license:other",
"region:us"
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"en"
] | TAGS
#gguf #chat #text-generation #en #dataset-m-a-p/CodeFeedback-Filtered-Instruction #base_model-Qwen/CodeQwen1.5-7B-Chat #license-other #region-us
| CodeQwen1.5-7B-Chat - SOTA GGUF
===============================
* Model creator: Qwen
* Original model: CodeQwen1.5-7B-Chat
Description
-----------
This repo contains State Of The Art quantized GGUF format model files for CodeQwen1.5-7B-Chat.
Quantization was done with an importance matrix that was trained for ... | [
"### How to load this model in Python code, using llama-cpp-python\n\n\nFor full documentation, please see: llama-cpp-python docs.",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple llama-cpp-python example code\n\n\nCodeQwen1.5-7B-Chat\n==========... | [
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null | null |
<!-- 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. -->
# V0424HMA9
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA9", "results": []}]} | Litzy619/V0424HMA9 | null | [
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"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
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#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
| V0424HMA9
=========
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0624
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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null | null |
<!-- 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. -->
# V0424HMA10
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA10", "results": []}]} | Litzy619/V0424HMA10 | null | [
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"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-24T14:42:19+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
| V0424HMA10
==========
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1353
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | rPucs/gemma-2b-relation-extracter-webnlg | null | [
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"endpoints_compatible",
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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null | transformers |
# 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 generate... | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["not-for-all-audiences"]} | AyoubELFallah/SEBN-blenderbot-distill-finetuned | null | [
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## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
... | {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | GeorgeImmanuel/stick_catching_dog | null | [
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"onnx",
"Huggy",
"deep-reinforcement-learning",
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#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you te... | [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* wh... | [
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DreadPoor/sphynx-7B-ties
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up... | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "Weyaxi/Einstein-v6-7B", "S-miguel/The-Trinity-Coder-7B"], "base_model": "DreadPoor/sphynx-7B-ties", "quantized_by": "mradermacher"} | mradermacher/sphynx-7B-ties-GGUF | null | [
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| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF file... | [] | [
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] |
text-generation | transformers |
<br/>
# 千尋 7B v0.1
Zebrafish 7B 加上 Breeze 7B 的 slerp merge 試驗性通用繁中基座模型 📚
GGUF Quants 👉 [Chihiro-7B-v0.1-GGUF](https://huggingface.co/yuuko-eth/Chihiro-7B-v0.1-GGUF)
請用 Mistral 7B Instruct 或是 Breeze 7B Instruct 所推薦的 Prompt 格式進行操作；以下為模型配置。
### Chihiro 7B v0.1
This is an expe... | {"language": ["zh", "en"], "license": "unknown", "tags": ["nlp", "chinese", "mistral", "traditional_chinese", "merge", "mergekit", "MediaTek-Research/Breeze-7B-Instruct-v0_1", "mlabonne/Zebrafish-7B"], "model_name": "Chihiro-7B-v0.1", "inference": false, "pipeline_tag": "text-generation", "prompt_template": "<s> SYS_PR... | yuuko-eth/Chihiro-7B-v0.1 | null | [
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"zh",
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"license:unknown",
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"text-generation-inference",
"region... | null | 2024-04-24T14:44:32+00:00 | [] | [
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|
千尋 7B v0.1
==========
Zebrafish 7B 加上 Breeze 7B 的 slerp merge 試驗性通用繁中基座模型
GGUF Quants Chihiro-7B-v0.1-GGUF
請用 Mistral 7B Instruct 或是 Breeze 7B Instruct 所推薦的 Prompt 格式進行操作；以下為模型配置。
![](https://i.URL
### Chihiro 7B v0.1
This is an experimental Mistral-architecture SLERP merge with two brilliant base model... | [
"### Chihiro 7B v0.1\n\n\nThis is an experimental Mistral-architecture SLERP merge with two brilliant base models. Zebrafish and Breeze were used together in this work.\n\n\nModel configuration is as follows:\n\n\n* Breeze-7B-Instruct as base.\n* Zebrafish-7B as model 1.\n\n\nTo use the model, please use either pro... | [
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null | transformers | # LewdPlay-8B
May 1st 2024: GGUF have been fixed with [this PR of llama.cpp](https://github.com/ggerganov/llama.cpp/pull/6920)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The new EVOLVE merge method was used (on MMLU specifically), see below for more in... | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["vicgalle/Roleplay-Llama-3-8B", "Undi95/Llama-3-Unholy-8B-e4", "Undi95/Llama-3-LewdPlay-8B"]} | Undi95/Llama-3-LewdPlay-8B-evo-GGUF | null | [
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"license:cc-by-nc-4.0",
"endpoints_compatible",
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| # LewdPlay-8B
May 1st 2024: GGUF have been fixed with this PR of URL
This is a merge of pre-trained language models created using mergekit.
The new EVOLVE merge method was used (on MMLU specifically), see below for more information!
Unholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, ... | [
"# LewdPlay-8B\n\nMay 1st 2024: GGUF have been fixed with this PR of URL\n\nThis is a merge of pre-trained language models created using mergekit.\n\nThe new EVOLVE merge method was used (on MMLU specifically), see below for more information!\n\nUnholy was used for uncensoring, Roleplay Llama 3 for the DPO train he... | [
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text-generation | null |
# Chihiro-7B-v0.1-GGUF
- Model creator: [yuuko-eth](https://huggingface.co/yuuko-eth)
- Original model: [Chihiro-7B-v0.1](https://huggingface.co/yuuko-eth/Chihiro-7B-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Chihiro-7B-v0.1](https://huggingface.co/yuuko-eth/Chih... | {"language": ["zh", "en"], "license": "unknown", "tags": ["nlp", "chinese", "mistral", "traditional_chinese", "merge", "mergekit", "MediaTek-Research/Breeze-7B-Instruct-v0_1", "mlabonne/Zebrafish-7B"], "model_name": "Chihiro-7B-v0.1", "inference": false, "pipeline_tag": "text-generation", "prompt_template": "<s> SYS_PR... | yuuko-eth/Chihiro-7B-v0.1-GGUF | null | [
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"mlabonne/Zebrafish-7B",
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| Chihiro-7B-v0.1-GGUF
====================
* Model creator: yuuko-eth
* Original model: Chihiro-7B-v0.1
Description
-----------
This repo contains GGUF format model files for Chihiro-7B-v0.1.
### About GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which... | [
"### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.... | [
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text-generation | transformers |
# **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 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
##... | {"language": ["en"], "license": "llama2", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2"], "extra_gated_heading": "You need to share contact information with Meta to access this model", "extra_gated_prompt": "### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\"Agreement\" means the terms and conditions for use, repr... | Userb1az/llama2-13b-fp16 | null | [
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"2307.09288"
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#transformers #pytorch #llama #text-generation #facebook #meta #llama-2 #en #arxiv-2307.09288 #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 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 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
... | [] | [
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | bhuvanmdev/falcon-7b-chess-beta | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Funded by [optional]:
- Shared by [optional]:
- Model type:
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- License... | [
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null | transformers |
# Uploaded model
- **Developed by:** hanifsyarubany10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/un... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | hanifsyarubany10/gemma-7b-100epochs-Unsloth-LaMini-2e-4 | null | [
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|
# Uploaded model
- Developed by: hanifsyarubany10
- License: apache-2.0
- Finetuned from model : unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"... | PabloVD/Reinforce-cartpole-1 | null | [
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#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Budbrain/budllama3-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a ... | {"language": ["en"], "license": "llama3", "library_name": "transformers", "base_model": "Budbrain/budllama3-8b", "quantized_by": "mradermacher"} | mradermacher/budllama3-8b-GGUF | null | [
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"en"
] | TAGS
#transformers #gguf #en #base_model-Budbrain/budllama3-8b #license-llama3 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF file... | [] | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2_l2arctic
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2_l2arctic", "results": []}]} | nrshoudi/wav2vec2_l2arctic | null | [
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"endpoints_compatible",
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] | null | 2024-04-24T14:52:25+00:00 | [] | [] | TAGS
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| wav2vec2\_l2arctic
==================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5487
* Wer: 0.1460
* Cer: 0.0904
Model description
-----------------
More information needed
Intended uses & lim... | [
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text-classification | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | kangXn/enta-tp-mde | null | [
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"safetensors",
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"text-classification",
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|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Kemasu/albert-base-v2-finetuned-squad-v3
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2)... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "albert-base-v2", "model-index": [{"name": "Kemasu/albert-base-v2-finetuned-squad-v3", "results": []}]} | Kemasu/albert-base-v2-finetuned-squad-v3 | null | [
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| Kemasu/albert-base-v2-finetuned-squad-v3
========================================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
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* Train Loss: 0.6522
* Train End Logits Accuracy: 0.8133
* Train Start Logits Accuracy: 0.7716
* Epoc... | [
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null | null |
<!-- 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. -->
# V0424HMA11
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA11", "results": []}]} | Litzy619/V0424HMA11 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-24T14:56:21+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
| V0424HMA11
==========
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1412
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
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null | peft |
<!-- 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. -->
# riddle-bot-v1
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta... | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "riddle-bot-v1", "results": []}]} | llm-wizard/riddle-bot-v1 | null | [
"peft",
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"sft",
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"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
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] | null | 2024-04-24T15:00:38+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us
|
# riddle-bot-v1
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# riddle-bot-v1\n\nThis model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | ahmed807762/gemma-2b-vetdataset-finetuned-v2 | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
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- Language(s) (NLP):
- License... | [
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text-generation | transformers | # Model Card
## Summary
SE.02 Is mxersion's most advanced model with over 100B parameters.
| Model Name | Description |
|:-----------------------------------------------------------------------------------|:----------------|
| [mxersion/SE.02... | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llm", "large language model", "100B", "100B parameters", "mxersion"], "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico", "pipeline_tag": "text-generation"} | Mxytyu/SE.02 | null | [
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| Model Card
==========
Summary
-------
SE.02 Is mxersion's most advanced model with over 100B parameters.
This model was trained using mxersion LLM.
Model Architecture
------------------
The details of the model architecture are:
Usage
-----
To use the model with the 'transformers' library on a machine w... | [
"### Open LLM Leaderboard",
"### MT-Bench\n\n\n!image/png\n\n\nDisclaimer\n----------\n\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n\n* Biases and Offensiveness:... | [
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null | peft |
<!-- 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-Meta-Llama-3-8B-qlora-pos
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta... | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "results-Meta-Llama-3-8B-qlora-pos", "results": []}]} | AlienKevin/Meta-Llama-3-8B-qlora-pos | null | [
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#peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
| results-Meta-Llama-3-8B-qlora-pos
=================================
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4308
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Docum... | {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | UXAIR/poca-SoccerTwos | null | [
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"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | null | 2024-04-24T15:03:38+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* ... | [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short ... | [
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null | transformers |
# Uploaded model
- **Developed by:** akumaburn
- **License:** apache-2.0
- **Un-finetuned model :** unsloth/llama-3-8b-bnb-4bit
This llama model was quantized from https://huggingface.co/unsloth/llama-3-8b-bnb-4bit/tree/main without any finetuning.
Using [Unsloth](https://github.com/unslothai/unsloth) and Huggingf... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | akumaburn/llama-3-8b-bnb-4bit-GGUF | null | [
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|
# Uploaded model
- Developed by: akumaburn
- License: apache-2.0
- Un-finetuned model : unsloth/llama-3-8b-bnb-4bit
This llama model was quantized from URL without any finetuning.
Using Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": ["unsloth"]} | mlho/lora | null | [
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"endpoints_compatible",
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"1910.09700"
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#transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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null | transformers |
<!-- 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. -->
# donut-base-sroie-metrics-combined-new
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/n... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut-base-sroie-metrics-combined-new", "results": []}]} | davelotito/donut-base-sroie-metrics-combined-new | null | [
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"vision-encoder-decoder",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"license:mit",
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#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us
| donut-base-sroie-metrics-combined-new
=====================================
This model is a fine-tuned version of naver-clova-ix/donut-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3400
* Bleu score: 0.0856
* Precisions: [0.8478260869565217, 0.8017817371937639, 0.77551... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 2\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
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text-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": []}]} | taoyoung/distilbert-base-uncased-distilled-clinc | null | [
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"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
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"region:us"
] | null | 2024-04-24T15:06:25+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2715
* Accuracy: 0.9465
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
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text-generation | transformers |
## VAGO solutions Llama-3-SauerkrautLM-70b-Instruct
Introducing **Llama-3-SauerkrautLM-70b-Instruct** – our Sauerkraut version of the powerful [meta-llama/Meta-Llama-3-70B-Instruct](https://hug... | {"language": ["de", "en"], "license": "other", "tags": ["dpo"], "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution an... | VAGOsolutions/Llama-3-SauerkrautLM-70b-Instruct | null | [
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"conversational",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-24T15:06:37+00:00 | [] | [
"de",
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| !SauerkrautLM
VAGO solutions Llama-3-SauerkrautLM-70b-Instruct
------------------------------------------------
Introducing Llama-3-SauerkrautLM-70b-Instruct – our Sauerkraut version of the powerful meta-llama/Meta-Llama-3-70B-Instruct!
The model Llama-3-SauerkrautLM-70b-Instruct is a joint effort between VAGO So... | [
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-toxic2nontoxic-100-50-nodecay | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jondurbin/bagel-8b-v1.0
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-8b-v1.0-i1-... | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["llama-3", "bagel"], "datasets": ["ai2_arc", "allenai/ultrafeedback_binarized_cleaned", "argilla/distilabel-intel-orca-dpo-pairs", "jondurbin/airoboros-3.2", "codeparrot/apps", "facebook/belebele", "bluemoon-fandom-1-1-rp-cleaned", "boolq... | mradermacher/bagel-8b-v1.0-GGUF | null | [
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-----
static quants of URL
weighted/imatrix quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. ... | [] | [
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reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TheWalder/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | TheWalder/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-24T15:08:52+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
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] |
null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": ["unsloth"]} | chillies/mistral-vn-legal-chat | null | [
"transformers",
"safetensors",
"unsloth",
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"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | ai-maker-space/riddle-bot-v1 | null | [
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"region:us"
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"1910.09700"
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#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sa... | {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_ga... | SparkleDark/rl_course_vizdoom_health_gathering_supreme | null | [
"sample-factory",
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"reinforcement-learning",
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"region:us"
] | null | 2024-04-24T15:12:14+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the mod... | [
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"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\... | [
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | SamaahKhan/bart-before-fine-tuning | null | [
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"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TheWalder/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- ... | TheWalder/Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-24T15:14:43+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
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] |
text-classification | transformers |
# Spanish Sentiment Analysis Classifier
## Overview
This BERT-based text classifier was developed as a thesis project for the Computer Engineering degree at Universidad de Buenos Aires (UBA).
The model is designed to detect sentiments in Spanish and was fine-tuned on the *dccuchile/bert-base-spanish-wwm-uncased* mod... | {"language": ["es"], "license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "Te quiero. Te amo", "output": [{"label": "Positive", "score": 1.0}, {"label": "Negative", "score": 0.0}]}]} | VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis | null | [
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"safetensors",
"bert",
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"es",
"license:apache-2.0",
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#transformers #safetensors #bert #text-classification #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Spanish Sentiment Analysis Classifier
## Overview
This BERT-based text classifier was developed as a thesis project for the Computer Engineering degree at Universidad de Buenos Aires (UBA).
The model is designed to detect sentiments in Spanish and was fine-tuned on the *dccuchile/bert-base-spanish-wwm-uncased* mod... | [
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null | peft |
<!-- 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. -->
# falcon7binstruct_bylaw_test_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://hug... | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "falcon7binstruct_bylaw_test_oct23", "results": []}]} | omar-sala7/falcon7binstruct_bylaw_test_oct23 | null | [
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"license:apache-2.0",
"region:us"
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#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us
|
# falcon7binstruct_bylaw_test_oct23
This model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
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null | null |
# Llama-3-8B-NLI-ties
Llama-3-8B-NLI-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [/content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained](https://huggingface.co//content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained)
* [/content/drive/MyDrive/llama3_label_r... | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "/content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained", "/content/drive/MyDrive/llama3_label_rationale_pretrained3"]} | pwei07/Llama-3-8B-NLI-ties | null | [
"merge",
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"/content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained",
"/content/drive/MyDrive/llama3_label_rationale_pretrained3",
"license:apache-2.0",
"region:us"
] | null | 2024-04-24T15:18:01+00:00 | [] | [] | TAGS
#merge #mergekit #lazymergekit #/content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained #/content/drive/MyDrive/llama3_label_rationale_pretrained3 #license-apache-2.0 #region-us
|
# Llama-3-8B-NLI-ties
Llama-3-8B-NLI-ties is a merge of the following models using mergekit:
* /content/drive/MyDrive/llama3_anli1_rationale_ft_pretrained
* /content/drive/MyDrive/llama3_label_rationale_pretrained3
## Configuration
| [
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text-classification | setfit |
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the S... | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "widget": [{"text": "this is a story of two misfits who do n't stand a chance alone , but together they... | Christina0824/setfit-test | null | [
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| SetFit with sentence-transformers/paraphrase-mpnet-base-v2
==========================================================
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text-generation | peft |
### Model Description
- **Developed by:** [Microsoft]
- **Model type:** [Text Generation]
- **Finetuned from model** [microsoft/phi-1_5]
## How to Use
Phi-1.5 has been integrated in the transformers version 4.30.0. ensure that you are doing the following:
* When loading the model, ensure that trust_remote_code=Tru... | {"language": ["en"], "license": "mit", "library_name": "peft", "tags": ["nlp", "code"], "pipeline_tag": "text-generation"} | SSTalha/Fashion_PHI_1-5 | null | [
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### Model Description
- Developed by: [Microsoft]
- Model type: [Text Generation]
- Finetuned from model [microsoft/phi-1_5]
## How to Use
Phi-1.5 has been integrated in the transformers version 4.30.0. ensure that you are doing the following:
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null | transformers |
# Uploaded model
- **Developed by:** manjunathshiva
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
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/un... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | manjunathshiva/llama3_8b_kannada_lora_model | null | [
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# Uploaded model
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- License: apache-2.0
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This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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text-generation | transformers |
# OpenELM
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce **OpenELM**, a family of **Open**-source **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling s... | {"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"} | TommyZQ/OpenELM-1_1B | null | [
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| OpenELM
=======
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce OpenELM, a family of Open-source Efficient Language Models. OpenELM uses a layer-wise scaling strategy to ef... | [
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | xuliu15/openai-whisper-small-frisian-colab | null | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | CMU-AIR2/math-deepseek_FULL_HardArith_Interm-FTMWP-FULL | null | [
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text-generation | transformers |
<!-- 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. -->
# stablelm-2-1_6b-spin-dpo-3-full
This model was trained from scratch on an unknown dataset.
## Model description
More informati... | {"tags": ["trl", "dpo", "generated_from_trainer"], "model-index": [{"name": "stablelm-2-1_6b-spin-dpo-3-full", "results": []}]} | nnheui/stablelm-2-1_6b-spin-dpo-3-full | null | [
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# stablelm-2-1_6b-spin-dpo-3-full
This model was trained from scratch 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
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null | transformers |
# Listwise MonoBERT trained on Baidu-ULTR using the Dual Learning Algorithm (DLA)
A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with a **listwise DLA objective on clicks**. Following [Ai et al.](https://arxiv.org/abs/1804.05938), the dual learning algorithm j... | {"license": "mit", "datasets": ["philipphager/baidu-ultr-pretrain", "philipphager/baidu-ultr_uva-mlm-ctr"], "metrics": ["log-likelihood", "dcg@1", "dcg@3", "dcg@5", "dcg@10", "ndcg@10", "mrr@10"], "co2_eq_emissions": {"emissions": 2090, "source": "Calculated using the [ML CO2 impact calculator](https://mlco2.github.io/... | philipphager/baidu-ultr_uva-bert_dla | null | [
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| Listwise MonoBERT trained on Baidu-ULTR using the Dual Learning Algorithm (DLA)
===============================================================================
A flax-based MonoBERT cross encoder trained on the Baidu-ULTR dataset with a listwise DLA objective on clicks. Following Ai et al., the dual learning algorith... | [] | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# Whisper Large TR - Özgün Tosun
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whi... | {"language": ["tr"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "Whisper Large TR - \u00d6zg\u00fcn Tosun", "results": [{"task": {"type": "automatic-speech-recogn... | ozguntosun/whisper-large-v3-tr | null | [
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| Whisper Large TR - Özgün Tosun
==============================
This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1323
* Wer: 11.7279
Model description
-----------------
More information needed
Inten... | [
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null | transformers |
# Two Tower MonoBERT trained on Baidu-ULTR
A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with an **additivie two tower architecture** as suggested by [Yan et al](https://research.google/pubs/revisiting-two-tower-models-for-unbiased-learning-to-rank/). Similar... | {"license": "mit", "datasets": ["philipphager/baidu-ultr-pretrain", "philipphager/baidu-ultr_uva-mlm-ctr"], "metrics": ["log-likelihood", "dcg@1", "dcg@3", "dcg@5", "dcg@10", "ndcg@10", "mrr@10"], "co2_eq_emissions": {"emissions": 2090, "source": "Calculated using the [ML CO2 impact calculator](https://mlco2.github.io/... | philipphager/baidu-ultr_uva-bert_twotower | null | [
"transformers",
"safetensors",
"bert",
"dataset:philipphager/baidu-ultr-pretrain",
"dataset:philipphager/baidu-ultr_uva-mlm-ctr",
"arxiv:2207.03051",
"arxiv:2404.02543",
"license:mit",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2024-04-24T15:27:46+00:00 | [
"2207.03051",
"2404.02543"
] | [] | TAGS
#transformers #safetensors #bert #dataset-philipphager/baidu-ultr-pretrain #dataset-philipphager/baidu-ultr_uva-mlm-ctr #arxiv-2207.03051 #arxiv-2404.02543 #license-mit #co2_eq_emissions #endpoints_compatible #region-us
| Two Tower MonoBERT trained on Baidu-ULTR
========================================
A flax-based MonoBERT cross encoder trained on the Baidu-ULTR dataset with an additivie two tower architecture as suggested by Yan et al. Similar to a position-based click model (PBM), a two tower model jointly learns item relevance (wi... | [] | [
"TAGS\n#transformers #safetensors #bert #dataset-philipphager/baidu-ultr-pretrain #dataset-philipphager/baidu-ultr_uva-mlm-ctr #arxiv-2207.03051 #arxiv-2404.02543 #license-mit #co2_eq_emissions #endpoints_compatible #region-us \n"
] | [
90
] | [
"TAGS\n#transformers #safetensors #bert #dataset-philipphager/baidu-ultr-pretrain #dataset-philipphager/baidu-ultr_uva-mlm-ctr #arxiv-2207.03051 #arxiv-2404.02543 #license-mit #co2_eq_emissions #endpoints_compatible #region-us \n"
] |
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