modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
marcus2000/my_extractive_model_v4 | 2023-10-31T20:37:35.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v4 | 0 | 2 | transformers | 2023-10-31T20:20:06 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 30
config["num_labels"] = 2
config["batch_size"] = 3
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 4
config["learning_rate"] = 5e-6
config["warmup_steps"] = 2
config["num_train_epochs"] = 20 | 417 | [
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thomasz8/ppo-LunarLander-v2 | 2023-10-31T21:31:00.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | thomasz8 | null | null | thomasz8/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-31T21:30:40 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.35 +/- 21.49
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-qlora-4bit | 2023-10-31T22:06:34.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-qlora-4bit | 0 | 2 | peft | 2023-10-31T22:06:11 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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TheBloke/Skywork-13B-base-GGUF | 2023-10-31T22:26:05.000Z | [
"transformers",
"skywork",
"arxiv:2310.19341",
"arxiv:2310.16713",
"license:other",
"region:us"
] | null | TheBloke | null | null | TheBloke/Skywork-13B-base-GGUF | 7 | 2 | transformers | 2023-10-31T22:15:50 | ---
base_model: Skywork/Skywork-13B-base
inference: false
license: other
license_link: https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf
license_name: license
model_creator: Skywork
model_name: Skywork 13B Base
model_type: skywork
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Skywork 13B Base - GGUF
- Model creator: [Skywork](https://huggingface.co/Skywork)
- Original model: [Skywork 13B Base](https://huggingface.co/Skywork/Skywork-13B-base)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Skywork's Skywork 13B Base](https://huggingface.co/Skywork/Skywork-13B-base).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Skywork-13B-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF)
* [Skywork's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Skywork/Skywork-13B-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [skywork-13b-base.Q2_K.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q2_K.gguf) | Q2_K | 2 | 5.82 GB| 8.32 GB | smallest, significant quality loss - not recommended for most purposes |
| [skywork-13b-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q3_K_S.gguf) | Q3_K_S | 3 | 6.07 GB| 8.57 GB | very small, high quality loss |
| [skywork-13b-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q3_K_M.gguf) | Q3_K_M | 3 | 6.78 GB| 9.28 GB | very small, high quality loss |
| [skywork-13b-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q3_K_L.gguf) | Q3_K_L | 3 | 7.40 GB| 9.90 GB | small, substantial quality loss |
| [skywork-13b-base.Q4_0.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q4_0.gguf) | Q4_0 | 4 | 7.87 GB| 10.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [skywork-13b-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q4_K_S.gguf) | Q4_K_S | 4 | 7.91 GB| 10.41 GB | small, greater quality loss |
| [skywork-13b-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q4_K_M.gguf) | Q4_K_M | 4 | 8.40 GB| 10.90 GB | medium, balanced quality - recommended |
| [skywork-13b-base.Q5_0.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q5_0.gguf) | Q5_0 | 5 | 9.57 GB| 12.07 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [skywork-13b-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q5_K_S.gguf) | Q5_K_S | 5 | 9.57 GB| 12.07 GB | large, low quality loss - recommended |
| [skywork-13b-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q5_K_M.gguf) | Q5_K_M | 5 | 9.84 GB| 12.34 GB | large, very low quality loss - recommended |
| [skywork-13b-base.Q6_K.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q6_K.gguf) | Q6_K | 6 | 11.37 GB| 13.87 GB | very large, extremely low quality loss |
| [skywork-13b-base.Q8_0.gguf](https://huggingface.co/TheBloke/Skywork-13B-base-GGUF/blob/main/skywork-13b-base.Q8_0.gguf) | Q8_0 | 8 | 14.72 GB| 17.22 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Skywork-13B-base-GGUF and below it, a specific filename to download, such as: skywork-13b-base.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Skywork-13B-base-GGUF skywork-13b-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Skywork-13B-base-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Skywork-13B-base-GGUF skywork-13b-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m skywork-13b-base.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Skywork-13B-base-GGUF", model_file="skywork-13b-base.Q4_K_M.gguf", model_type="skywork", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Skywork's Skywork 13B Base
<!-- <div align="center">
<h1>
✨Skywork
</h1>
</div> -->
<div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div>
<p align="center">
👨💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a>• 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a>
</p>
<div align="center">
[🎉天工在线对话平台已正式向公众开放](https://sso.tiangong.cn/?redirect=https://model-platform.tiangong.cn/overview&client_id=200005)
</div>
<div align="center">
[](https://github.com/SkyworkAI/Skywork/stargazers)
[](https://github.com/SkyworkAI/Skywork/fork)
</div>
# 模型介绍（Introduction）
**Skywork-13B-Base**模型在高质量清洗过滤的3.2万亿个多语言（主要是中文和英文）和代码数据上进行预训练，它在多种评测和各种基准测试上都展现了同等规模模型的最佳效果。
**Skywork-13B-Base**: The model was trained on a high-quality cleaned dataset consisting of 3.2 trillion multilingual data (mainly Chinese and English) and code. It has demonstrated the best performance among models of similar scale in various evaluations and benchmark tests.
如果您希望了解更多的信息，如训练方案，评估方法，请参考我们的[技术报告](http://arxiv.org/abs/2310.19341)，[Skymath](https://arxiv.org/abs/2310.16713)论文，[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。
If you are interested in more training and evaluation details, please refer to our [technical report](http://arxiv.org/abs/2310.19341), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper.
## 训练数据（Training Data）
我们精心搭建了数据清洗流程对文本中的低质量数据、有害信息、敏感信息进行清洗过滤。我们的Skywork-13B-Base模型是在清洗后的3.2TB高质量中、英、代码数据上进行训练，其中英文占比52.2%，中文占比39.6%，代码占比8%，在兼顾中文和英文上的表现的同时，代码能力也能有保证。
We have developed a data cleaning pipeline with great care to effectively clean and filter low-quality data and eliminate harmful information from text data. Our Skywork-13B-Base model is trained on a dataset with 3.2TB tokens that consists of high-quality Chinese, English, and code data, all of which have been thoroughly cleaned. The English data comprises 52.2% of the dataset, the Chinese data accounts for 39.6%, and the code data makes up 8%. This comprehensive approach ensures optimal performance for both Chinese and English while also maintaining the ability to handle code.
| | Category | Percentage |
|-------------|------------------|------------|
| **English** | Webpages | 39.8% |
| | Books | 3.6% |
| | Academic Papers | 3.0% |
| | Encyclopedia | 0.5% |
| | Miscellany | 2.9% |
| **Chinese** | Webpages | 30.4% |
| | Social Media | 5.5% |
| | Encyclopedia | 0.8% |
| | Miscellany | 3.1% |
| **Other Lang.** | Encyclopedia | 2.4% |
| **Code** | Github | 8.0% |
## 模型结构（Model Structure）
与Llama-2-13B模型对比，天工Skywork-13B模型采用相对更加瘦长的网络结构，层数为52层，同时将FFN Dim和Hidden Dim缩小到12288和4608，从而保证模型参数量和原始Llama-2-13B模型相当。根据我们前期实验对比，相对瘦长的网络结构在大Batch Size训练下可以取得更好的泛化效果。Skywork-13B和Llama-2-13B模型的对比如下：
Compared to the Llama2-13B model, the Skywork-13B model adopts a relatively thinner and deeper network structure with 52 layers. At the same time, the FFN Dim and Hidden Dim are reduced to 12288 and 4608, respectively, to ensure that the model has a similar number of parameters as the original Llama-13B model. Based on our preliminary experimental results, a relatively thinner and deeper network structure can achieve better generalization performance under large batch size training. The detailed comparison between the Skywork-13B and Llama-2-13B models is as follows:
| Model Structure | Llama2-13B | Skywork-13B |
|----------------------|:----:|:-----------:|
| Vocab. Size | 32,000 | 65,536 |
| Hidden Dim. | 5,120 | 4,608 |
| FFN Dim. | 13,696 | 12,288 |
| Head Dim. | 128 | 128 |
| Num. Heads | 40 | 36 |
| Num. Layers | 40 | 52 |
| Seq. Len. | 4,096 | 4,096 |
| Positional Embedding | RoPE | RoPE |
## 分词器（Tokenizer）
我们使用Byte-Pair Encoding（BPE）对数据进行分词，词表大小为65536，其中拉丁字符和子词为32000个，汉字和Unicode符号8000个，汉语词语25519个，剩下的17个为保留字。
We use Byte-Pair Encoding (BPE) to tokenize the data, with a vocabulary size of 65536. Among them, there are 32000 Latin characters and subwords, 8000 Chinese characters and Unicode symbols, 25519 Chinese words, and the remaining 17 are reserved words.
| Category | Size |
|---------------------------------|--------|
| Latin based words & subwords | 32000 |
| Chinese characters & Unicode symbols | 8000 |
| Chinese words | 25519 |
| Reserved symbols | 17 |
| **Total** | **65536** |
# 模型评估（Evaluation）
## 领域数据困惑度评估（Perplexity Evaluaiton）
语言模型训练的本质上是让预测下一个词更准确。基于这个认知，我们认为评估基础大模型一个重要的方式是评估在各大领域上语言模型生成文章的概率。在模型训练中预测下一个词的概率一般使用Cross Entropy损失函数，整体的损失函数为每个位置预测真实词损失的平均，则有：
$$loss = \sum^{n}_{i=1} log(p_i) / n = log( \prod_{i=1}^n p_i) / n$$
其中$n$是文档的长度，即token数，$p_i$是位置i上真实词的概率，我们知道文档中每一个位置上真实词的概率的联乘则为生成该文档的概率，如此我们就将loss和生成文章的概率联系在了一起。而不同模型因为使用的分词器不同，具有不同的token数，因此对损失函数乘以token数目$n$，这样就仅考虑生成文章的概率部分，不同模型也可以进行比较。我们将标准化后loss取指数转换成perplexity，使得模型的差异更加可读。为了阅读方便后续提到的loss和ppl为模型标准化后的loss和perplexity。
基于上述分析，我们对对多个领域筛选出2023年9月份新发布的几百到上千篇高质量文章，并人工进行了核对。保证所有的测试数据不在天工模型以及其他所有模型的训练集中，并且测试数据的来源也足够广泛，质量也高。我们可以选取当前最新的文章评测不同模型的ppl，模型很难作弊。
下图列出了不同开源模型，天工Skywork-13B-Base取得最优效果，证明了我们的Base模型的基础能力处于国内开源模型中文最强水平。
We have chosen several hundred to thousands of high-quality articles that were published after September 1, 2023 across various fields. We have manually verified these articles to ensure their quality. It is important to note that none of the test data used in evaluating the Skywork model or any other models is included in their training set. Furthermore, the test data is diverse and of high quality, making it challenging for the models to gain an unfair advantage.
The figure below displays the performance of different open source models. Skywork-13B-Base achieves the best results.
| | Tech | Movie | Gov. | Game | Finance | General | Average |
|------------------|-------|-------|-------|-------|---------|---------|---------|
| MOSS-7B | 20.83 | 39.66 | 11.08 | 31.24 | 10.59 | 13.25 | 18.50 |
| InternLM-7B | 13.43 | 24.90 | 5.88 | 19.78 | 6.17 | 8.10 | 11.17 |
| Qwen-7B | 13.39 | 25.16 | 5.55 | 19.26 | 5.76 | 7.78 | 10.83 |
| Baichuan2-7B | 12.89 | 23.26 | 5.34 | 18.36 | 5.68 | 7.62 | 10.41 |
| LLaMA2-13B | 23.26 | 50.66 | 18.09 | 32.52 | 14.85 | 16.55 | 23.54 |
| Xverse-13B | 12.55 | 23.49 | 5.20 | 17.69 | 5.54 | 7.46 | 10.19 |
| Baichuan-13B | 12.38 | 22.46 | 5.21 | 17.59 | 5.42 | 7.37 | 10.03 |
| Baichuan2-13B | 12.14 | 21.85 | 5.05 | 17.15 | 5.35 | 7.24 | 9.81 |
| Qwen-14B | 11.90 | 22.43 | 4.89 | **16.94** | 5.24 | 7.03 | 9.67 |
| InternLM-20B | 12.34 | 22.06 | 5.75 | 17.45 | 5.73 | 7.78 | 10.34 |
| Aquila2-34B | 14.62 | 29.09 | 5.72 | 21.78 | 5.83 | 8.45 | 11.73 |
| Skywork-13B-Base | **11.58** | **21.84** | **4.76** | 17.28 | **4.92** | **6.82** | **9.42** |
### 评测数据和评测脚本（Loss Evaluation）
我们将评测数据和评测脚本也进行了开源，下载github上的代码运行下面命令则可以复现我们的结果。
We have also open-sourced the data and evaluation scripts. You can reproduce our results by running the following command.
```
bash bash_scripts/skywork_eval_loss.sh
```
## Benchmark评估（Benchmark Results）
我们评估了各大权威评测benchmark上的结果作为参考，包括C-Eval，MMLU，CMMLU，GSM8K。遵循之前的评估流程，C-Eval、MMLU、CMMLU测试5-shot结果，GSM8K测试8-shot结果。可以看到Skywork-13B-Base模型在中文开源模型中处于前列，在同等参数规模下为最优水平。
We evaluated Skywork-13B-Base on several popular benchmarks, including C-Eval, MMLU, CMMLU, and GSM8K. Following the previous evaluation process, we tested the 5-shot results of C-Eval, MMLU, and CMMLU, and the 8-shot results of GSM8K. It can be seen that the Skywork-13B-Base model is among the top models in the Chinese open source model community, performing at an optimal level with the same parameter scale.
| Model | C-Eval | CMMLU | MMLU | GSM8K |
|-------------------------|:-----:|:---------------:|:----------:|:-------:|
| LLaMA-1-13B-Base | 35.5 | 31.2 | 46.9 | 17.8 |
| Open-LLaMA-13B | 27.1 | 26.7 | 42.7 | 12.4 |
| LLaMA-2-13B-Base | 36.5 | 36.6 | 54.8 | 28.7 |
| InternLM-20B | 58.8 | - | 62.0 | 52.6 |
| Qwen-14B-Base | 72.1 | 71.0 | 66.3 | 61.3 |
| Aquila2-34B-Base | 63.1 | 71.4 | 64.2 | 58.4 |
| XVERSE-13B-Base | 54.7 | - | 55.1 | - |
| Baichuan-13B-Base | 52.4 | 55.3 | 51.6 | 26.6 |
| Baichuan-2-13B-Base | 58.1 | 62.0 | 59.2 | 52.3 |
| Skywork-13B-Base (ours) | 60.6 | 61.8 | 62.1 | 55.8 |
## Benchmark评估详细结果
我们给出**Skywork-13B-Base**模型在C-Eval，CMMLU，MMLU上模型的详细结果。
We provide detailed results of the Skywork-13B-Base model on C-EVAL, CMMLU, and MMLU.
| Benchmark | **STEM** | **Humanities** | **Social Science** | **Other** | **China Specific** | **Hard** | **Average** |
|:-----:|:---------:|:--------:|:-------------:|:--------:|:--------:|:--------:|:--------:|
| **C-EVAL** | 51.2 | 67.8 | 74.6 | 57.5 | - | 39.4 | 60.6 |
| **CMMLU** | 49.5 | 69.3 | 65.9 | 63.3 | 64.2 | - | 61.8 |
| **MMLU** | 51.6 | 58.0 | 72.5 | 68.8 | - | - | 62.1 |
# 快速开始（Quickstart）
我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。
We have open-sourced the model parameters, configuration files, tokenizer, and more on Huggingface and Modelscope.
## 依赖安装（Requirements）
- Python 3.8及以上版本
- Pytorch 2.0及以上版本
- CUDA建议使用11.4以上版本。
Skywork-13B-Base模型，Skywork-13B-Chat模型和Skywork-13B-Math模型运行下面的脚本进行Python依赖安装。
- Python 3.8 and above
- Pytorch 2.0 and above
- CUDA 11.4 and above are recommended.
Skywork-13B-Base model, Skywork-13B-Chat model, and Skywork-13B-Math model run the following script for Python dependency installation:
```shell
pip install -r requirements.txt
```
## Huggingface模型测试（Demonstration）
### Base 模型推理（Base Model Inference）
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> from transformers.generation import GenerationConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("SkyworkAI/Skywork-13B-Base", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("SkyworkAI/Skywork-13B-Base", device_map="auto", trust_remote_code=True).eval()
>>> inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device)
>>> response = model.generate(inputs.input_ids, max_length=128)
>>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True))
陕西的省会是西安，西安是我国著名的古都，在历史上有十三个朝代在此建都，所以西安又被称为“十三朝古都”。西安是我国著名的旅游城市，每年都有大量的游客来到西安旅游，西安的旅游资源非常丰富，有很多著名的旅游景点，比如秦始皇兵马俑、大雁塔、华清池、大唐芙蓉园、西安城墙、大明宫国家遗址公园、西安碑林博物馆、西安钟楼、西安鼓楼、西安半坡博物馆、西安大兴善寺、西安小雁塔
>>> inputs = tokenizer('陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州', return_tensors='pt').to(model.device)
>>> response = model.generate(inputs.input_ids, max_length=128)
>>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True))
陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州，湖北的省会是武汉，湖南的省会是长沙，江西的省会是南昌，安徽的省会是合肥，江苏的省会是南京，浙江的省会是杭州，福建的省会是福州，广东的省会是广州，广西的省会是南宁，海南的省会是海口，四川的省会是成都，贵州的省会是贵阳，云南的省会是昆明，西藏的省会是拉萨，青海的省会是西宁，宁夏的省会是银川，新疆的省会是乌鲁木齐。
```
# 模型微调（Fine-tuning）
## 全量微调（Full-parameter Fine-tuning）
使用Skywork-13B-Base模型进行预训练微调
```bash
## preprocess continue pretraining data
## Because pre-training data is usually large, we use a script to process the training data separately.
python train/pt_data_preprocess.py \
-t $MODEL_PATH \
-i data/pt_train.jsonl \
-o data_cache/pt_train_demo
## launch training
export WANDB_API_KEY=YOUR_WANDB_KEY
export WANDB_ENTITY=skywork
export WANDB_PROJECT=skywork-13b-opensource
export MODEL_PATH=skywork-13b-models/skywork-13b-base
export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train
bash bash_scripts/skywork_13b_pt.sh
```
使用Skywork-13B-Base模型进行有监督微调（SFT, Supevise Fine-tuning）
```bash
## preprocess data and launch training
export WANDB_API_KEY=YOUR_WANDB_KEY
export WANDB_ENTITY=skywork
export WANDB_PROJECT=skywork-13b-opensource
export SFT_DATA_DIR=data/sft_data
export DATA_CACHE_DIR=data_cache/sft_train_demo
bash bash_scripts/skywork_13b_sft.sh
```
## LoRA微调（PEFT）
使用Skywork-13B-Base模型以及LoRA进行预训练微调
```bash
## preprocess continue pretraining data
## Because pre-training data is usually large, we use a script to process the training data separately.
python train/pt_data_preprocess.py \
-t $MODEL_PATH \
-i data/pt_train.jsonl \
-o data_cache/pt_train_demo
export WANDB_API_KEY=YOUR_WANDB_KEY
export WANDB_ENTITY=skywork
export WANDB_PROJECT=skywork-13b-opensource
export MODEL_PATH=skywork-13b-models/skywork-13b-base
export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train
bash bash_scripts/skywork_13b_pt_lora.sh
```
使用Skywork-13B-Base模型以及LoRA进行有监督微调（SFT, Supevise Fine-tuning）
```bash
export WANDB_API_KEY=YOUR_WANDB_KEY
export WANDB_ENTITY=skywork
export WANDB_PROJECT=skywork-13b-opensource
export SFT_DATA_DIR=data/sft_data
export DATA_CACHE_DIR=data_cache/sft_train_demo
bash bash_scripts/skywork_13b_sft_lora.sh
```
# 声明和协议（Declaration and License Agreement）
## 声明（Declaration）
我们在此声明，不要利用Skywork模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Skywork 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用skywork开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
We hereby declare that the Skywork model should not be used for any activities that pose a threat to national or societal security or engage in unlawful actions. Additionally, we request users not to deploy the Skywork model for internet services without appropriate security reviews and records. We hope that all users will adhere to this principle to ensure that technological advancements occur in a regulated and lawful environment.
We have done our utmost to ensure the compliance of the data used during the model's training process. However, despite our extensive efforts, due to the complexity of the model and data, there may still be unpredictable risks and issues. Therefore, if any problems arise as a result of using the Skywork open-source model, including but not limited to data security issues, public opinion risks, or any risks and problems arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility.
## 协议（License Agreement）
社区使用Skywork模型需要遵循[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)。Skywork模型支持商业用途，如果您计划将Skywork模型或其衍生品用于商业目的，无需再次申请， 但请您仔细阅读[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)并严格遵守相关条款。
The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf).
[《Skywork 模型社区许可协议》》]:https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf
[skywork-opensource@kunlun-inc.com]: mailto:skywork-opensource@kunlun-inc.com
# 引用和联系我们（Contact Us and Citation）
如果您觉得我们的工作对您有帮助，欢迎引用我们的论文~
If you find our work helpful, please feel free to cite our paper~
```
@misc{wei2023skywork,
title={Skywork: A More Open Bilingual Foundation Model},
author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou},
year={2023},
eprint={2310.19341},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@article{skyworkmath,
title={SkyMath: Technical Report},
author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu},
journal={arXiv preprint arXiv: 2310.16713},
url={https://arxiv.org/abs/2310.16713},
year={2023}
}
```
```
@article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023,
author = {Skywork Multi-Modal Group},
month = sep,
title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}},
year = {2023}
}
```
<!-- original-model-card end -->
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alessiodm/a2c-PandaReachDense-v3 | 2023-10-31T23:57:51.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | alessiodm | null | null | alessiodm/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-31T23:52:27 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.25 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
lucas-meyer/seq-xls-r-fleurs_zu-run3-asr_xh-run7-LM | 2023-11-02T10:38:05.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:lucas-meyer/asr_xh",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | lucas-meyer | null | null | lucas-meyer/seq-xls-r-fleurs_zu-run3-asr_xh-run7-LM | 0 | 2 | transformers | 2023-11-01T00:25:13 | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: seq-xls-r-fleurs_zu-run3-asr_xh-run7-LM
results: []
datasets:
- lucas-meyer/asr_xh
---
<!-- 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. -->
# seq-xls-r-fleurs_zu-run3-asr_xh-run7-LM
This model is a fine-tuned version of [lucas-meyer/xls-r-fleurs_zu-run3](https://huggingface.co/lucas-meyer/xls-r-fleurs_zu-run3) on asr_af with an added LM (5-gram Kneser-Ney).
It achieves the following results:
- Loss: 0.3923
- Wer: 0.3993
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3 | 740 | [
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PCYP/ppo-LunarLander-v2 | 2023-11-01T01:00:53.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PCYP | null | null | PCYP/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T01:00:31 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 224.54 +/- 69.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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thejosephloy/my_awesome_qa_model | 2023-11-01T04:35:08.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | thejosephloy | null | null | thejosephloy/my_awesome_qa_model | 0 | 2 | transformers | 2023-11-01T02:39:41 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.226 | 1.0 | 4380 | 1.1317 |
| 0.8767 | 2.0 | 8760 | 1.0853 |
| 0.6474 | 3.0 | 13140 | 1.1427 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.14.0
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lucascardacci/my-hello-word-ppo-LunarLander-v2 | 2023-11-01T03:19:37.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lucascardacci | null | null | lucascardacci/my-hello-word-ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T03:19:00 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.59 +/- 16.93
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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mesolitica/malaysian-debertav3-small | 2023-11-02T01:38:14.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"feature-extraction",
"ms",
"endpoints_compatible",
"region:us"
] | feature-extraction | mesolitica | null | null | mesolitica/malaysian-debertav3-small | 0 | 2 | transformers | 2023-11-01T03:31:41 | ---
language:
- ms
---
# Pretrain SMALL 512 masking context length DebertaV3 on Malaysian text
README at https://github.com/mesolitica/malaya/tree/5.1/pretrained-model/deberta
WandB, https://wandb.ai/mesolitica/deberta-small?workspace=user-husein-mesolitica | 260 | [
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AmrMKayid/sb3-ppo-LunarLander-v2 | 2023-11-01T04:26:29.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | AmrMKayid | null | null | AmrMKayid/sb3-ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T04:26:08 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.51 +/- 16.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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PedroSampaio/fruits-360-16-7 | 2023-11-01T13:12:22.000Z | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | PedroSampaio | null | null | PedroSampaio/fruits-360-16-7 | 0 | 2 | transformers | 2023-11-01T04:47:57 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fruits-360-16-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fruits-360-16-7
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0718 | 1.0 | 1057 | 0.0188 | 0.9976 |
| 0.0135 | 2.0 | 2115 | 0.0055 | 0.9992 |
| 0.0236 | 3.0 | 3173 | 0.0077 | 0.9976 |
| 0.0082 | 4.0 | 4231 | 0.0026 | 0.9992 |
| 0.004 | 5.0 | 5288 | 0.0036 | 0.9988 |
| 0.0067 | 6.0 | 6346 | 0.0024 | 0.9991 |
| 0.0005 | 7.0 | 7399 | 0.0022 | 0.9992 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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hpandana/a2c-PandaReachDense-v3 | 2023-11-01T05:01:40.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | hpandana | null | null | hpandana/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-01T04:55:36 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
kimnice/bald-man-lora-from-stable-diffusion-v1-5 | 2023-11-01T06:29:42.000Z | [
"diffusers",
"text-to-image",
"autotrain",
"region:us"
] | text-to-image | kimnice | null | null | kimnice/bald-man-lora-from-stable-diffusion-v1-5 | 0 | 2 | diffusers | 2023-11-01T06:07:54 |
---
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: photo of a myLora man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
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] |
kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3 | 2023-11-02T14:06:31.000Z | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | kanishka | null | null | kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3 | 0 | 2 | transformers | 2023-11-01T06:53:32 | ---
base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3/config.json
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3
This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-3e-3/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1401
- Accuracy: 0.4346
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.363 | 1.0 | 18353 | 3.4874 | 0.3868 |
| 3.1973 | 2.0 | 36706 | 3.3426 | 0.4014 |
| 3.0719 | 3.0 | 55059 | 3.2462 | 0.4142 |
| 3.0021 | 4.0 | 73412 | 3.1971 | 0.4191 |
| 2.9417 | 5.0 | 91765 | 3.1619 | 0.4232 |
| 2.9009 | 6.0 | 110118 | 3.1445 | 0.4260 |
| 2.8633 | 7.0 | 128471 | 3.1366 | 0.4281 |
| 2.8297 | 8.0 | 146824 | 3.1401 | 0.4289 |
| 2.8018 | 9.0 | 165177 | 3.1246 | 0.4305 |
| 2.7728 | 10.0 | 183530 | 3.1126 | 0.4314 |
| 2.7451 | 11.0 | 201883 | 3.1144 | 0.4320 |
| 2.7292 | 12.0 | 220236 | 3.1135 | 0.4328 |
| 2.7022 | 13.0 | 238589 | 3.1158 | 0.4335 |
| 2.6729 | 14.0 | 256942 | 3.1189 | 0.4338 |
| 2.6553 | 15.0 | 275295 | 3.1106 | 0.4348 |
| 2.6339 | 16.0 | 293648 | 3.1190 | 0.4351 |
| 2.6095 | 17.0 | 312001 | 3.1272 | 0.4349 |
| 2.5845 | 18.0 | 330354 | 3.1280 | 0.4348 |
| 2.5668 | 19.0 | 348707 | 3.1316 | 0.4348 |
| 2.543 | 20.0 | 367060 | 3.1401 | 0.4346 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
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marcus2000/my_extractive_model_v5 | 2023-11-01T07:41:37.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v5 | 0 | 2 | transformers | 2023-11-01T07:39:49 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 30
config["num_labels"] = 2
config["batch_size"] = 3
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 4
config["learning_rate"] = 2e-6
config["warmup_steps"] = 2
config["num_train_epochs"] = 15 | 417 | [
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Arabic-Clip/arabertv2-Vit-B-16-plus-epoch-5-trained-3M-5M-from-scratch | 2023-11-01T08:35:13.000Z | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | Arabic-Clip | null | null | Arabic-Clip/arabertv2-Vit-B-16-plus-epoch-5-trained-3M-5M-from-scratch | 0 | 2 | transformers | 2023-11-01T08:04:23 | iew run experiment-bert-large-arabertv2-Vit-B-16-plus-240-2023 10 29 - 12 50 22 at: https://wandb.ai/uos_mlalp/mscoco_teacher_learning_full_data/runs/3v12yy0p | 158 | [
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Akshay0706/Cinnamon-Plant-Model-Final | 2023-11-01T08:40:58.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Akshay0706 | null | null | Akshay0706/Cinnamon-Plant-Model-Final | 0 | 2 | transformers | 2023-11-01T08:39:40 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Cinnamon-Plant-Model-Final
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Cinnamon-Plant-Model-Final
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0348
- eval_accuracy: 0.9796
- eval_runtime: 8.12
- eval_samples_per_second: 6.034
- eval_steps_per_second: 1.601
- epoch: 187.0
- step: 1683
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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GowthamMl/mistral-sql-generation | 2023-11-01T08:47:23.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | GowthamMl | null | null | GowthamMl/mistral-sql-generation | 0 | 2 | peft | 2023-11-01T08:47:03 | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
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imalexianne/test_trainer | 2023-11-01T11:31:08.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | imalexianne | null | null | imalexianne/test_trainer | 0 | 2 | transformers | 2023-11-01T09:41:53 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8163
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7394 | 0.5 | 500 | 0.6840 |
| 0.6632 | 1.0 | 1000 | 0.5994 |
| 0.5285 | 1.5 | 1500 | 0.6457 |
| 0.5023 | 2.0 | 2000 | 0.5899 |
| 0.3422 | 2.5 | 2500 | 0.7876 |
| 0.3087 | 3.0 | 3000 | 0.8163 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Tokenizers 0.14.1
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GluonsGluonsGluons/ppo-LunarLander-v2 | 2023-11-01T10:13:47.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | GluonsGluonsGluons | null | null | GluonsGluonsGluons/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T10:13:25 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.34 +/- 21.57
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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marcus2000/my_extractive_model_v6 | 2023-11-01T10:31:40.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v6 | 0 | 2 | transformers | 2023-11-01T10:30:00 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 100
config["num_labels"] = 2
config["batch_size"] = 3
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 4
config["learning_rate"] = 1e-6
config["warmup_steps"] = 2
config["num_train_epochs"] = 100 | 419 | [
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minoosh/AST-finetuned-on-shEMO_speech | 2023-11-01T12:50:15.000Z | [
"transformers",
"pytorch",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | audio-classification | minoosh | null | null | minoosh/AST-finetuned-on-shEMO_speech | 0 | 2 | transformers | 2023-11-01T10:57:42 | ---
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: AST-finetuned-on-shEMO_speech
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AST-finetuned-on-shEMO_speech
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6988
- Accuracy: 0.7967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8657 | 1.0 | 75 | 0.7066 | 0.7867 |
| 0.6951 | 2.0 | 150 | 0.6622 | 0.7567 |
| 0.3368 | 3.0 | 225 | 0.5851 | 0.8433 |
| 0.1414 | 4.0 | 300 | 0.7233 | 0.79 |
| 0.1011 | 5.0 | 375 | 0.8763 | 0.7967 |
| 0.0438 | 6.0 | 450 | 0.9009 | 0.8067 |
| 0.0108 | 7.0 | 525 | 1.0540 | 0.83 |
| 0.0033 | 8.0 | 600 | 1.0177 | 0.81 |
| 0.0003 | 9.0 | 675 | 1.1074 | 0.84 |
| 0.0113 | 10.0 | 750 | 1.1107 | 0.8433 |
| 0.0002 | 11.0 | 825 | 1.1273 | 0.8367 |
| 0.0001 | 12.0 | 900 | 1.1634 | 0.8333 |
| 0.0001 | 13.0 | 975 | 1.1502 | 0.84 |
| 0.0045 | 14.0 | 1050 | 1.1541 | 0.84 |
| 0.0039 | 15.0 | 1125 | 1.1550 | 0.84 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.12.0+cu116
- Datasets 2.14.6
- Tokenizers 0.14.1
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nlamot/ppo-LunarLander-v2 | 2023-11-01T11:14:17.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nlamot | null | null | nlamot/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T11:13:56 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.80 +/- 23.44
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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T-Brockhouse/first_model | 2023-11-01T12:14:31.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | T-Brockhouse | null | null | T-Brockhouse/first_model | 0 | 2 | transformers | 2023-11-01T12:13:53 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: first_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# first_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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hugging-ecaf/ppo-LunarLander-v2 | 2023-11-01T12:57:48.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | hugging-ecaf | null | null | hugging-ecaf/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T12:57:27 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.15 +/- 17.94
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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Skier8402/wav2vec-xls-r | 2023-11-01T14:30:31.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"sw",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | Skier8402 | null | null | Skier8402/wav2vec-xls-r | 0 | 2 | transformers | 2023-11-01T12:58:35 | ---
language:
- sw
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec-xls-r
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sw
split: test
args: 'config: sw, split: train+test'
metrics:
- name: Wer
type: wer
value: 0.9982181245473462
---
<!-- 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. -->
# wav2vec-xls-r
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1585
- Wer: 0.9982
Increase the number of epochs to improve performance or use a bigger model.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0131 | 1.53 | 1000 | 3.0846 | 1.0 |
| 2.322 | 3.07 | 2000 | 2.6234 | 1.0000 |
| 1.3523 | 4.6 | 3000 | 2.2515 | 0.9991 |
| 1.1727 | 6.13 | 4000 | 2.1585 | 0.9982 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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PedroSampaio/vit-base-patch16-224-fruits-360-16-7 | 2023-11-01T15:12:53.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | PedroSampaio | null | null | PedroSampaio/vit-base-patch16-224-fruits-360-16-7 | 0 | 2 | transformers | 2023-11-01T13:25:12 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-fruits-360-16-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-fruits-360-16-7
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.04 | 1.0 | 1057 | 0.0216 | 0.9953 |
| 0.0053 | 2.0 | 2115 | 0.0081 | 0.9974 |
| 0.0252 | 3.0 | 3173 | 0.0043 | 0.9991 |
| 0.0221 | 4.0 | 4231 | 0.0038 | 0.9991 |
| 0.0116 | 5.0 | 5288 | 0.0010 | 1.0 |
| 0.0014 | 6.0 | 6346 | 0.0013 | 0.9997 |
| 0.0003 | 7.0 | 7399 | 0.0011 | 0.9996 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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PedroSampaio/vit-base-patch16-224-in21k-fruits-360-16-7 | 2023-11-01T15:24:31.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | PedroSampaio | null | null | PedroSampaio/vit-base-patch16-224-in21k-fruits-360-16-7 | 0 | 2 | transformers | 2023-11-01T13:30:34 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-fruits-360-16-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-fruits-360-16-7
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0131
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4608 | 1.0 | 1057 | 0.4181 | 0.9983 |
| 0.0699 | 2.0 | 2115 | 0.0649 | 0.9953 |
| 0.0313 | 3.0 | 3173 | 0.0243 | 0.9986 |
| 0.0143 | 4.0 | 4231 | 0.0131 | 0.9992 |
| 0.0121 | 5.0 | 5288 | 0.0103 | 0.9989 |
| 0.009 | 6.0 | 6346 | 0.0095 | 0.9988 |
| 0.0037 | 7.0 | 7399 | 0.0090 | 0.9989 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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ptamm/qa_model | 2023-11-01T13:47:08.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | ptamm | null | null | ptamm/qa_model | 0 | 2 | transformers | 2023-11-01T13:44:50 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 32 | 3.7167 |
| No log | 2.0 | 64 | 3.3052 |
| No log | 3.0 | 96 | 3.1981 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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T-Brockhouse/first_model_more_data | 2023-11-01T14:01:43.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | T-Brockhouse | null | null | T-Brockhouse/first_model_more_data | 0 | 2 | transformers | 2023-11-01T13:52:07 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: first_model_more_data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# first_model_more_data
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing | 2023-11-01T14:02:30.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing | 0 | 2 | peft | 2023-11-01T14:02:25 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
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## Training procedure
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- PEFT 0.6.0.dev0
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
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] |
MakAttack/6540dc5ec89e47f9ae652663 | 2023-11-01T15:15:46.000Z | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"license:openrail++",
"region:us",
"has_space"
] | text-to-image | MakAttack | null | null | MakAttack/6540dc5ec89e47f9ae652663 | 0 | 2 | diffusers | 2023-11-01T14:12:38 |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MakAttack/6540dc5ec89e47f9ae652663
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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VoidZeroe/llama7-model | 2023-11-01T14:26:56.000Z | [
"peft",
"region:us"
] | null | VoidZeroe | null | null | VoidZeroe/llama7-model | 0 | 2 | peft | 2023-11-01T14:26:39 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- 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_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
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npc0/chatglm3-6b-int4 | 2023-11-01T16:57:46.000Z | [
"glm",
"chatglm",
"ggml",
"zh",
"en",
"region:us",
"has_space"
] | null | npc0 | null | null | npc0/chatglm3-6b-int4 | 0 | 2 | null | 2023-11-01T15:46:32 | ---
language:
- zh
- en
tags:
- glm
- chatglm
- ggml
---
# ChatGLM3-6B-int4
介绍 (Introduction)
ChatGLM3-6B 是 ChatGLM 系列最新一代的开源模型，[THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
用 [ChatGLM.CPP]() 基於 GGML quantize 生成 Q4_0、Q4_1 權重 weights 儲存於此倉庫。
## Performance
|Model |GGML quantize method| HDD size |1 token\*|
|----------------------|--------------------|----------|---------|
|chatglm3-ggml-q4_0.bin| q4_0 | 3.51 GB | 74ms |
|chatglm3-ggml-q4_1.bin| q4_1 | 3.9 GB | 77ms |
\* ms/token (CPU @ Platinum 8260) from [reference](https://github.com/li-plus/chatglm.cpp#performance)
## Getting Started
1. Install dependency
```sh
pip install chatglm-cpp transformers
```
2. Download weight
```sh
wget https://huggingface.co/npc0/chatglm3-6b-int4/resolve/main/chatglm3-ggml-q4_0.bin
```
3. Code
```py
import chatglm_cpp
pipeline = chatglm_cpp.Pipeline("./chatglm3-ggml-q4_0.bin")
pipeline.chat(["你好"])
# Output: 你好👋！我是人工智能助手 ChatGLM3-6B，很高兴见到你，欢迎问我任何问题。
``` | 1,047 | [
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Loriiis/ppo-LunarLander-v2-2 | 2023-11-01T16:19:47.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Loriiis | null | null | Loriiis/ppo-LunarLander-v2-2 | 0 | 2 | stable-baselines3 | 2023-11-01T16:19:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.10 +/- 21.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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JDCollier89/ppo-LunarLander-v2 | 2023-11-01T16:34:58.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | JDCollier89 | null | null | JDCollier89/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T16:34:38 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.01 +/- 13.46
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
TheBloke/vietnamese-llama2-7B-40GB-GGUF | 2023-11-01T16:58:03.000Z | [
"transformers",
"llama",
"vi",
"en",
"dataset:vietgpt/wikipedia_vi",
"dataset:wikipedia",
"dataset:pg19",
"dataset:mc4",
"license:other",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/vietnamese-llama2-7B-40GB-GGUF | 1 | 2 | transformers | 2023-11-01T16:53:58 | ---
base_model: bkai-foundation-models/vietnamese-llama2-7b-40GB
datasets:
- vietgpt/wikipedia_vi
- wikipedia
- pg19
- mc4
inference: false
language:
- vi
- en
license: other
model_creator: BKAI-HUST Foundation Models Lab
model_name: Vietnamese Llama2 7B 40GB
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Vietnamese Llama2 7B 40GB - GGUF
- Model creator: [BKAI-HUST Foundation Models Lab](https://huggingface.co/bkai-foundation-models)
- Original model: [Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB)
<!-- description start -->
## Description
This repo contains GGUF format model files for [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF)
* [BKAI-HUST Foundation Models Lab's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [vietnamese-llama2-7b-40gb.Q2_K.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q2_K.gguf) | Q2_K | 2 | 2.89 GB| 5.39 GB | smallest, significant quality loss - not recommended for most purposes |
| [vietnamese-llama2-7b-40gb.Q3_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_S.gguf) | Q3_K_S | 3 | 3.02 GB| 5.52 GB | very small, high quality loss |
| [vietnamese-llama2-7b-40gb.Q3_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_M.gguf) | Q3_K_M | 3 | 3.37 GB| 5.87 GB | very small, high quality loss |
| [vietnamese-llama2-7b-40gb.Q3_K_L.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_L.gguf) | Q3_K_L | 3 | 3.67 GB| 6.17 GB | small, substantial quality loss |
| [vietnamese-llama2-7b-40gb.Q4_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_0.gguf) | Q4_0 | 4 | 3.91 GB| 6.41 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [vietnamese-llama2-7b-40gb.Q4_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_K_S.gguf) | Q4_K_S | 4 | 3.94 GB| 6.44 GB | small, greater quality loss |
| [vietnamese-llama2-7b-40gb.Q4_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_K_M.gguf) | Q4_K_M | 4 | 4.16 GB| 6.66 GB | medium, balanced quality - recommended |
| [vietnamese-llama2-7b-40gb.Q5_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_0.gguf) | Q5_0 | 5 | 4.74 GB| 7.24 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [vietnamese-llama2-7b-40gb.Q5_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_K_S.gguf) | Q5_K_S | 5 | 4.74 GB| 7.24 GB | large, low quality loss - recommended |
| [vietnamese-llama2-7b-40gb.Q5_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_K_M.gguf) | Q5_K_M | 5 | 4.87 GB| 7.37 GB | large, very low quality loss - recommended |
| [vietnamese-llama2-7b-40gb.Q6_K.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q6_K.gguf) | Q6_K | 6 | 5.63 GB| 8.13 GB | very large, extremely low quality loss |
| [vietnamese-llama2-7b-40gb.Q8_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q8_0.gguf) | Q8_0 | 8 | 7.29 GB| 9.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/vietnamese-llama2-7B-40GB-GGUF and below it, a specific filename to download, such as: vietnamese-llama2-7b-40gb.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF vietnamese-llama2-7b-40gb.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF vietnamese-llama2-7b-40gb.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m vietnamese-llama2-7b-40gb.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/vietnamese-llama2-7B-40GB-GGUF", model_file="vietnamese-llama2-7b-40gb.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB
We employed [SentencePiece](https://github.com/google/sentencepiece) to retrain a Vietnamese tokenizer with a vocabulary size of 20K. No Vietnamese word segmentation was used. We then merged this vocabulary with the original one of Llama2, removing duplicate tokens.
The new tokenizer significantly improves when encoding Vietnamese text, reducing the number of tokens by 50% compared to ChatGPT and approximately 70% compared to the original Llama2.
We conducted a single-epoch continual pretraining, also known as incremental pretraining, using the Llama2-chat 7B model on a mixed dataset totaling 40.5 GB, comprised of:
- 19 GB [NewsCorpus](https://github.com/binhvq/news-corpus)
- 1.1 GB Vietnamese Wikipedia
- 1.6 GB [Vietnamese books](https://www.kaggle.com/datasets/iambestfeeder/10000-vietnamese-books)
- 4.5 GB Vietnamese legal documents (crawled from thuvienphapluat and processed by ourselves)
- 2.1 GB Vietnamese legal text (from [C4-vi](https://huggingface.co/datasets/c4))
- 1.1 GB English Books (sub-sampled from [pg19](https://huggingface.co/datasets/pg19))
- 1.1 GB English Wikipedia (sub-sampled from 20220301.en wikipedia)
- 10 GB English Text (sub-sampled from [C4-en](https://huggingface.co/datasets/c4))
We trained the model on a DGX A100 system, utilizing four GPU A100 in 10 days (about 1000 GPU hours).
Hyperparameters are set as follows:
- Training Regime: BFloat16 mixed precision
- Lora Config:
```
{
"base_model_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
"bias": "none",
"enable_lora": null,
"fan_in_fan_out": false,
"inference_mode": true,
"lora_alpha": 32.0,
"lora_dropout": 0.05,
"merge_weights": false,
"modules_to_save": [
"embed_tokens",
"lm_head"
],
"peft_type": "LORA",
"r": 8,
"target_modules": [
"q_proj",
"v_proj",
"k_proj",
"o_proj",
"gate_proj",
"down_proj",
"up_proj"
],
"task_type": "CAUSAL_LM"
}
```
We also provide the [LoRA part](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB/tree/main/pt_lora_model) so that you can integrate it with the original Llama2-chat-7b by yourself.
Please note that **this model requires further supervised fine-tuning (SFT)** to be used in practice!
Usage and other considerations: We refer to the [Llama 2](https://github.com/facebookresearch/llama)
Training loss:
<img src="figure/training_loss.png" alt="Training Loss Curve"/>
**Disclaimer**
This project is built upon Meta's Llama-2 model. It is essential to strictly adhere to the open-source license agreement of Llama-2 when using this model. If you incorporate third-party code, please ensure compliance with the relevant open-source license agreements.
It's important to note that the content generated by the model may be influenced by various factors, such as calculation methods, random elements, and potential inaccuracies in quantification. Consequently, this project does not offer any guarantees regarding the accuracy of the model's outputs, and it disclaims any responsibility for consequences resulting from the use of the model's resources and its output.
For those employing the models from this project for commercial purposes, developers must adhere to local laws and regulations to ensure the compliance of the model's output content. This project is not accountable for any products or services derived from such usage.
**Acknowledgments**
We extend our gratitude to PHPC - Phenikaa University and NVIDIA for their generous provision of computing resources for model training. Our appreciation also goes out to binhvq and the other authors for their diligent efforts in collecting and preparing the Vietnamese text corpus.
<!-- original-model-card end -->
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Milana/my_awesome_wnut_model | 2023-11-01T17:00:41.000Z | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Milana | null | null | Milana/my_awesome_wnut_model | 0 | 2 | transformers | 2023-11-01T16:59:44 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
model-index:
- name: my_awesome_wnut_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
daochf/Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v02 | 2023-11-01T17:20:03.000Z | [
"peft",
"region:us"
] | null | daochf | null | null | daochf/Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v02 | 0 | 2 | peft | 2023-11-01T17:20:00 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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arindampande/ppo-LunarLander-v2 | 2023-11-01T17:23:25.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | arindampande | null | null | arindampande/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T17:23:06 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.12 +/- 17.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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reneeshdenny/ppo-LunarLander-v2 | 2023-11-01T17:29:46.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | reneeshdenny | null | null | reneeshdenny/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-01T17:29:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.10 +/- 19.78
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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AbhishekRP2002/CodeLlama-34B-hf-fine-tuned-api-testing | 2023-11-01T18:27:03.000Z | [
"peft",
"region:us"
] | null | AbhishekRP2002 | null | null | AbhishekRP2002/CodeLlama-34B-hf-fine-tuned-api-testing | 0 | 2 | peft | 2023-11-01T17:57:36 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
| 464 | [
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] |
VoidZeroe/llama8-model | 2023-11-01T18:35:05.000Z | [
"peft",
"region:us"
] | null | VoidZeroe | null | null | VoidZeroe/llama8-model | 0 | 2 | peft | 2023-11-01T18:34:31 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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loraxian/cm40-xs4d-6h2t-0 | 2023-11-01T19:08:23.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | loraxian | null | null | loraxian/cm40-xs4d-6h2t-0 | 0 | 2 | transformers | 2023-11-01T18:54:07 | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain | 120 | [
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CyberRift/sidekick-mitre | 2023-11-01T20:21:49.000Z | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"llm",
"large language model",
"en",
"dataset:HoangCuongNguyen/CTI-to-MITRE-question-answers",
"text-generation-inference",
"region:us"
] | text-generation | CyberRift | null | null | CyberRift/sidekick-mitre | 0 | 2 | transformers | 2023-11-01T20:17:10 | ---
language:
- en
library_name: transformers
tags:
- llm
- large language model
inference: false
thumbnail: null
datasets:
- HoangCuongNguyen/CTI-to-MITRE-question-answers
---
# Model Card
## Summary
- Base model: [CyberRift/sidekick](https://huggingface.co/CyberRift/sidekick)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="CyberRift/sidekick-mitre",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?<|endoftext|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"CyberRift/sidekick-mitre",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"CyberRift/sidekick-mitre",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "CyberRift/sidekick-mitre" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?<|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2048)
(emb_dropout): Dropout(p=0.0, inplace=False)
(layers): ModuleList(
(0-23): 24 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
(post_attention_dropout): Dropout(p=0.0, inplace=False)
(post_mlp_dropout): Dropout(p=0.0, inplace=False)
(attention): GPTNeoXAttention(
(rotary_emb): GPTNeoXRotaryEmbedding()
(query_key_value): Linear(in_features=2048, out_features=6144, bias=True)
(dense): Linear(in_features=2048, out_features=2048, bias=True)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2048, out_features=8192, bias=True)
(dense_4h_to_h): Linear(in_features=8192, out_features=2048, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2048, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please 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.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | 8,029 | [
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ishwarbb23/my_awesome_model | 2023-11-02T03:32:20.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | ishwarbb23 | null | null | ishwarbb23/my_awesome_model | 0 | 2 | transformers | 2023-11-01T20:17:15 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PHQ_Q1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4815
- Accuracy: 0.8025
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 51 | 0.4601 | 0.7975 |
| No log | 2.0 | 102 | 0.4815 | 0.8025 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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globalgoalsdirectory/stella-base-en-v2-setfit-sdg7 | 2023-11-01T20:29:48.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | globalgoalsdirectory | null | null | globalgoalsdirectory/stella-base-en-v2-setfit-sdg7 | 0 | 2 | sentence-transformers | 2023-11-01T20:29:32 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
labels:
- 'False'
- 'True'
---
# globalgoalsdirectory/stella-base-en-v2-setfit-sdg7
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("globalgoalsdirectory/stella-base-en-v2-setfit-sdg7")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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awkyu/audiogen-medium | 2023-11-01T21:24:19.000Z | [
"transformers",
"musicgen",
"text-to-audio",
"arxiv:2209.15352",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-audio | awkyu | null | null | awkyu/audiogen-medium | 0 | 2 | transformers | 2023-11-01T21:05:31 | ---
license: cc-by-nc-4.0
---
# AudioGen - Medium - 1.5B
AudioGen is an autoregressive transformer LM that synthesizes general audio conditioned on text (Text-to-Audio).
Internally, AudioGen operates over discrete representations learnt from the raw waveform, using an EnCodec tokenizer.
AudioGen was presented at [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352) by *Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi*.
AudioGen 1.5B is a variant of the original AudioGen model that follows [MusicGen](https://arxiv.org/abs/2306.05284) architecture.
More specifically, it is trained over a 16kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz with a delay pattern between the codebooks.
Having only 50 auto-regressive steps per second of audio, this AudioGen model allows faster generation while reaching similar performances to the original AudioGen model introduced in the paper.
## Audiocraft Usage
You can run AudioGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
```
pip install git+https://github.com/facebookresearch/audiocraft.git
```
2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed:
```
apt get install ffmpeg
```
3. Run the following Python code:
```py
import torchaudio
from audiocraft.models import AudioGen
from audiocraft.data.audio import audio_write
model = AudioGen.get_pretrained('facebook/audiogen-medium')
model.set_generation_params(duration=5) # generate 5 seconds.
descriptions = ['dog barking', 'sirenes of an emergency vehicule', 'footsteps in a corridor']
wav = model.generate(descriptions) # generates 3 samples.
for idx, one_wav in enumerate(wav):
# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
```
## Model details
See [AudioGen's model card](https://github.com/facebookresearch/audiocraft/blob/main/model_cards/AUDIOGEN_MODEL_CARD.md). | 2,209 | [
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marcus2000/my_extractive_model_v7 | 2023-11-02T00:25:49.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v7 | 0 | 2 | transformers | 2023-11-01T23:43:11 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 100
config["num_labels"] = 2
config["batch_size"] = 3
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 4
config["learning_rate"] = 1e-6
config["warmup_steps"] = 2
config["num_train_epochs"] = 10 | 418 | [
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ayman2002rahman/ppo-SnowballTarget | 2023-11-01T23:47:11.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | ayman2002rahman | null | null | ayman2002rahman/ppo-SnowballTarget | 0 | 2 | ml-agents | 2023-11-01T23:47:08 | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **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-Documentation/
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 teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ayman2002rahman/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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ayman2002rahman/ppo-Pyramids | 2023-11-01T23:59:44.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | ayman2002rahman | null | null | ayman2002rahman/ppo-Pyramids | 0 | 2 | ml-agents | 2023-11-01T23:57:54 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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/
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 teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ayman2002rahman/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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] |
guydebruyn/bert-finetuned-squad | 2023-11-02T02:59:20.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | guydebruyn | null | null | guydebruyn/bert-finetuned-squad | 0 | 2 | transformers | 2023-11-02T00:34:09 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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yesj1234/jako_mbartLarge_13p_tokenize_run1 | 2023-11-02T00:54:59.000Z | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"ja",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | yesj1234 | null | null | yesj1234/jako_mbartLarge_13p_tokenize_run1 | 0 | 2 | transformers | 2023-11-02T00:51:28 | ---
language:
- ja
- ko
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: jako_13p_tokenie_run1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# jako_13p_tokenie_run1
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3560
- Bleu: 50.2114
- Gen Len: 18.2159
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.2125 | 0.83 | 1600 | 1.1356 | 44.2732 | 18.9394 |
| 0.8519 | 1.66 | 3200 | 1.0618 | 47.1622 | 18.3936 |
| 0.6394 | 2.49 | 4800 | 1.0923 | 47.7818 | 18.3397 |
| 0.532 | 3.32 | 6400 | 1.1294 | 48.4283 | 18.3375 |
| 0.3543 | 4.15 | 8000 | 1.1765 | 47.7916 | 18.4422 |
| 0.2569 | 4.99 | 9600 | 1.2103 | 48.1268 | 18.5385 |
| 0.1732 | 5.82 | 11200 | 1.2549 | 48.9329 | 18.2085 |
| 0.1228 | 6.65 | 12800 | 1.3022 | 49.0248 | 18.2133 |
| 0.0937 | 7.48 | 14400 | 1.3179 | 49.3503 | 18.1673 |
| 0.0627 | 8.31 | 16000 | 1.3409 | 49.5551 | 18.2672 |
| 0.0558 | 9.14 | 17600 | 1.3545 | 49.7808 | 18.2815 |
| 0.0442 | 9.97 | 19200 | 1.3560 | 50.2114 | 18.2159 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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jguy235/ppo-LunarLander-v2 | 2023-11-02T01:48:50.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jguy235 | null | null | jguy235/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T01:48:30 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.80 +/- 23.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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cuongptnk/ppo-LunarLander-v2 | 2023-11-02T01:57:49.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | cuongptnk | null | null | cuongptnk/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T01:57:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.79 +/- 18.61
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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VoidZeroe/llama9-model | 2023-11-02T02:09:58.000Z | [
"peft",
"region:us"
] | null | VoidZeroe | null | null | VoidZeroe/llama9-model | 0 | 2 | peft | 2023-11-02T02:09:24 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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knarasi1/a2c-PandaReachDense-v3 | 2023-11-02T02:16:03.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | knarasi1 | null | null | knarasi1/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-02T02:09:57 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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sanjana-m/wav2vec2-large-xls-r-300m-gn-pt-colab | 2023-11-02T12:01:49.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | sanjana-m | null | null | sanjana-m/wav2vec2-large-xls-r-300m-gn-pt-colab | 0 | 2 | transformers | 2023-11-02T02:43:34 | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: wav2vec2-large-xls-r-300m-gn-pt-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-gn-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5610
- eval_wer: 0.9291
- eval_runtime: 39.0397
- eval_samples_per_second: 20.774
- eval_steps_per_second: 2.613
- epoch: 26.01
- step: 13200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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MarBar/a2c-PandaReachDense-v3 | 2023-11-02T03:37:14.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | MarBar | null | null | MarBar/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-02T03:31:43 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.27 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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sharkMeow/ADL_test | 2023-11-02T10:54:30.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | sharkMeow | null | null | sharkMeow/ADL_test | 0 | 2 | transformers | 2023-11-02T04:22:33 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: ADL_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ADL_test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9924
- Rouge-1: 19.8975
- Rouge-2: 7.0134
- Rouge-l: 18.2845
- Gen Len: 14.3786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:-------:|:-------:|
| 6.0773 | 1.0 | 1356 | 11.8835 | 4.1822 | 17.4884 | 6.039 | 16.4024 |
| 5.0033 | 2.0 | 2713 | 13.385 | 4.0972 | 18.9145 | 6.6873 | 17.5342 |
| 4.7707 | 3.0 | 4068 | 14 | 4.019 | 19.62 | 6.908 | 18.08 |
| 4.7391 | 4.0 | 5424 | 14.3786 | 3.992 | 19.9 | 7.013 | 18.28 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.5
- Tokenizers 0.14.1
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-2 | 2023-11-02T07:41:48.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-2 | 0 | 2 | peft | 2023-11-02T07:41:38 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
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## Training procedure
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## Training procedure
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] |
nekofura/llama-zerowan_7b | 2023-11-02T08:18:20.000Z | [
"peft",
"region:us"
] | null | nekofura | null | null | nekofura/llama-zerowan_7b | 0 | 2 | peft | 2023-11-02T08:17:21 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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elemosynov/ppo-LunarLander-v2 | 2023-11-02T09:24:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | elemosynov | null | null | elemosynov/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T09:23:51 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.85 +/- 18.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
Kooten/Utopia-13B-3bpw-h8-exl2 | 2023-11-02T14:14:03.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Kooten | null | null | Kooten/Utopia-13B-3bpw-h8-exl2 | 1 | 2 | transformers | 2023-11-02T12:33:30 | ---
license: cc-by-nc-4.0
---
## Description
Exllama 2 quant of [Undi95/Utopia-13B](https://huggingface.co/Undi95/Utopia-13B)
3 BPW, Head bit set to 8
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## VRAM
My VRAM usage with 13B models are:
| Bits per weight | Context | VRAM |
|--|--|--|
| 8bpw | 8k | 22gb |
| 8bpw | 4k | 19gb |
| 6bpw | 8k | 19gb |
| 6bpw | 4k | 16gb |
| 4bpw | 8k | 16gb |
| 4bpw | 4k | 13gb |
| 3bpw | 8k | 15gb |
| 3bpw | 4k | 12gb |
I have rounded up, these arent exact numbers, this is also on a windows machine, they should be slightly lower on linux.
| 730 | [
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nide01/finetuned_IMDB_1000 | 2023-11-02T16:51:21.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | nide01 | null | null | nide01/finetuned_IMDB_1000 | 0 | 2 | transformers | 2023-11-02T14:11:07 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_IMDB_1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_IMDB_1000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3430
- Accuracy: 0.84
- F1: 0.8400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Tokenizers 0.14.1
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ESGBERT/SocRoBERTa-social | 2023-11-03T16:00:39.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ESG",
"social",
"en",
"dataset:ESGBERT/social_2k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | ESGBERT | null | null | ESGBERT/SocRoBERTa-social | 0 | 2 | transformers | 2023-11-02T14:15:42 | ---
language: en
license: apache-2.0
datasets:
- ESGBERT/social_2k
tags:
- ESG
- social
---
# Model Card for SocRoBERTa-social
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain.
Using the [SocRoBERTa-base](https://huggingface.co/ESGBERT/SocRoBERTa-base) model as a starting point, the SocRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.
## How to Get Started With the Model
You can use the model with a pipeline for text classification:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import datasets
tokenizer_name = "ESGBERT/SocRoBERTa-social"
model_name = "ESGBERT/SocRoBERTa-social"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
print(pipe("We follow rigorous supplier checks to prevent slavery and ensure workers' rights."))
```
## More details can be found in the paper
```bibtex
@article{Schimanski23ESGBERT,
title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
author={Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
year={2023},
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514},
}
```
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sh-holmes/ppo-LunarLander-v2 | 2023-11-02T14:42:10.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sh-holmes | null | null | sh-holmes/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T14:41:51 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.58 +/- 19.30
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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IdoCK/DRL_HuggingFace | 2023-11-02T14:46:37.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | IdoCK | null | null | IdoCK/DRL_HuggingFace | 0 | 2 | stable-baselines3 | 2023-11-02T14:46:18 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.74 +/- 20.87
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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Juniplayground/Mistral_orca-7B-640_v2 | 2023-11-02T14:50:39.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Juniplayground | null | null | Juniplayground/Mistral_orca-7B-640_v2 | 0 | 2 | peft | 2023-11-02T14:50:31 | ---
library_name: peft
base_model: Open-Orca/Mistral-7B-OpenOrca
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0
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mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k | 2023-11-03T22:28:47.000Z | [
"transformers",
"llama",
"text-generation",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | mmnga | null | null | mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k | 0 | 2 | transformers | 2023-11-02T15:14:28 | ---
license: llama2
---
# japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k
[stabilityaiさんが公開しているjapanese-stablelm-base-ja_vocab-beta-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-ja_vocab-beta-7b)を
日本語のキャリブレーションセットで生成したGPTQモデルになります。
キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、
1kほどランダムサンプリングしたものと、
[ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)のinput/outputを計200ほど追加しています。
[mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k)
モデル一覧
GPTQ
[mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k)
[mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k)
GGUF
[mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf)
[mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf)
## Usage
~~~Bash
pip install auto-gptq==0.4.2 transformers
~~~
~~~python
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from transformers import AutoTokenizer
model_name_or_path = "mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k"
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
# Model
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0")
# Your test prompt
prompt = """今日の夕食のレシピをご紹介します。"""
print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_length=128)[0]))
~~~ | 1,865 | [
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joshswartz/model_1_wikihow_permuted_endings | 2023-11-02T15:23:51.000Z | [
"peft",
"region:us"
] | null | joshswartz | null | null | joshswartz/model_1_wikihow_permuted_endings | 0 | 2 | peft | 2023-11-02T15:23:22 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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Phando/switch-base-32-finetuned-copa | 2023-11-02T16:49:48.000Z | [
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | Phando | null | null | Phando/switch-base-32-finetuned-copa | 0 | 2 | transformers | 2023-11-02T15:41:49 | The switch-base-32 model was fine-tuned on the COPA dataset.
Validation accuracy: 68.00.
Prompt key in PromptSouce: "cause_effect". | 133 | [
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AperMesa/ppo-LunarLander-v2 | 2023-11-02T16:05:42.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | AperMesa | null | null | AperMesa/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T16:05:20 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 244.88 +/- 23.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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bartowski/zephyr-7b-beta-exl2 | 2023-11-02T19:17:49.000Z | [
"generated_from_trainer",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:mit",
"region:us"
] | null | bartowski | null | null | bartowski/zephyr-7b-beta-exl2 | 0 | 2 | null | 2023-11-02T16:06:05 | ---
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-beta
results: []
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: mistralai/Mistral-7B-v0.1
quantized_by: bartowski
---
## Exllama v2 Quantizations of zephyr-7b-beta
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
<a href="https://huggingface.co/bartowski/zephyr-7b-beta-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/zephyr-7b-beta-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/zephyr-7b-beta-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/zephyr-7b-beta-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `zephyr-7b-beta-exl2`:
```shell
mkdir zephyr-7b-beta-exl2
huggingface-cli download bartowski/zephyr-7b-beta-exl2 --local-dir zephyr-7b-beta-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir zephyr-7b-beta-exl2
huggingface-cli download bartowski/zephyr-7b-beta-exl2 --revision 4.0 --local-dir zephyr-7b-beta-exl2 --local-dir-use-symlinks False
```
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nickrobinson/distilbert-base-uncased-finetuned-imdb | 2023-11-02T16:21:34.000Z | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | nickrobinson | null | null | nickrobinson/distilbert-base-uncased-finetuned-imdb | 0 | 2 | transformers | 2023-11-02T16:12:10 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4966 |
| 2.5796 | 2.0 | 314 | 2.4282 |
| 2.5355 | 3.0 | 471 | 2.4510 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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abacusai/Giraffe-beta-13b-32k | 2023-11-02T17:22:23.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama2",
"arxiv:2308.10882",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | abacusai | null | null | abacusai/Giraffe-beta-13b-32k | 1 | 2 | transformers | 2023-11-02T16:17:42 | ---
tags:
- llama2
license: llama2
---
## Model Details
### Model Description
We have followed up on our previous training runs related to extending the context length
of Llama models. The associated github repository
https://github.com/abacusai/long-context
has some basic details on our approach and metrics. We have also published a paper on arXiv
that covers our experiments and analysis a lot more comprehensively.
http://arxiv.org/abs/2308.10882
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **Model type:** Transformer based autoregressive causal language model
- **License:** Llama 2 Community License: https://github.com/facebookresearch/llama/blob/main/LICENSE
- **Finetuned from model:** Llama V2 13B | 727 | [
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Lornng/Llama2-7B-QLoRA-cpgQA-adapter-text-gen-final | 2023-11-02T18:51:52.000Z | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"region:us"
] | null | Lornng | null | null | Lornng/Llama2-7B-QLoRA-cpgQA-adapter-text-gen-final | 0 | 2 | peft | 2023-11-02T17:08:06 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
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marcus2000/my_extractive_model_v10 | 2023-11-02T17:10:38.000Z | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v10 | 0 | 2 | transformers | 2023-11-02T17:09:16 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 100
config["num_labels"] = 2
config["batch_size"] = 3
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 4
config["learning_rate"] = 5e-6
config["warmup_steps"] = 2
config["num_train_epochs"] = 10 | 418 | [
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-3 | 2023-11-02T17:16:45.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-3 | 0 | 2 | peft | 2023-11-02T17:16:29 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
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## Training procedure
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- PEFT 0.6.0
## Training procedure
### Framework versions
- PEFT 0.6.0
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dhanush23/wav2vec2-audio-emotion-classification | 2023-11-02T18:32:40.000Z | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | dhanush23 | null | null | dhanush23/wav2vec2-audio-emotion-classification | 0 | 2 | transformers | 2023-11-02T17:53:58 | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-audio-emotion-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-audio-emotion-classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
marcus2000/my_extractive_model_v11 | 2023-11-02T18:17:18.000Z | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | marcus2000 | null | null | marcus2000/my_extractive_model_v11 | 0 | 2 | transformers | 2023-11-02T17:54:01 | config['tokens_model_name'] = 'IlyaGusev/rubert_ext_sum_gazeta'
config["max_source_tokens_count"] = 512
config["max_source_sentences_count"] = 100
config["num_labels"] = 2
config["batch_size"] = 10
config['gradient_accumulation_steps'] = 10
config["logging_steps"] = 2
config["eval_steps"] = 2
config["save_steps"] = 10
config["learning_rate"] = 5e-6
config["warmup_steps"] = 10
config["num_train_epochs"] = 50 | 421 | [
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borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0 | 2023-11-02T18:34:48.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"text-generation-inference",
"region:us"
] | text-generation | borggAI | null | null | borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0 | 0 | 2 | transformers | 2023-11-02T18:29:51 | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.8),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?<|im_end|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.8),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "borggAI/ehartford-dolphin-2-1-mistral-7b-2-1-0" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?<|im_end|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.8),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32002, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32002, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please 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.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | 8,158 | [
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] |
Jack-cpu/Mistral_7_Email_Qlora | 2023-11-02T18:45:31.000Z | [
"peft",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Mistral_7_Email_Qlora | 0 | 2 | peft | 2023-11-02T18:45:17 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
| 464 | [
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P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV6 | 2023-11-03T01:13:42.000Z | [
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | P4B10 | null | null | P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV6 | 0 | 2 | transformers | 2023-11-02T18:51:56 | ---
license: apache-2.0
base_model: mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
tags:
- generated_from_keras_callback
model-index:
- name: P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV6
results: []
---
<!-- 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. -->
# P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV6
This model is a fine-tuned version of [mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0264
- Train End Logits Accuracy: 1.0
- Train Start Logits Accuracy: 1.0
- Validation Loss: 0.0165
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 106, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3694 | 0.9434 | 0.9371 | 0.0261 | 1.0 | 1.0 | 0 |
| 0.0264 | 1.0 | 1.0 | 0.0165 | 1.0 | 1.0 | 1 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 | 2023-11-04T02:09:03.000Z | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | kanishka | null | null | kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 | 0 | 2 | transformers | 2023-11-02T18:52:15 | ---
base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4
This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1756
- Accuracy: 0.4274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.9239 | 1.0 | 18353 | 3.9992 | 0.3336 |
| 3.3918 | 2.0 | 36706 | 3.5081 | 0.3842 |
| 3.201 | 3.0 | 55059 | 3.3534 | 0.4017 |
| 3.1032 | 4.0 | 73412 | 3.2792 | 0.4094 |
| 3.0303 | 5.0 | 91765 | 3.2313 | 0.4146 |
| 2.9788 | 6.0 | 110118 | 3.2123 | 0.4176 |
| 2.9353 | 7.0 | 128471 | 3.1918 | 0.4199 |
| 2.8982 | 8.0 | 146824 | 3.1838 | 0.4220 |
| 2.868 | 9.0 | 165177 | 3.1760 | 0.4230 |
| 2.8384 | 10.0 | 183530 | 3.1689 | 0.4237 |
| 2.8118 | 11.0 | 201883 | 3.1658 | 0.4250 |
| 2.7971 | 12.0 | 220236 | 3.1678 | 0.4250 |
| 2.7705 | 13.0 | 238589 | 3.1651 | 0.4258 |
| 2.7438 | 14.0 | 256942 | 3.1691 | 0.4260 |
| 2.73 | 15.0 | 275295 | 3.1655 | 0.4264 |
| 2.712 | 16.0 | 293648 | 3.1646 | 0.4269 |
| 2.6921 | 17.0 | 312001 | 3.1692 | 0.4271 |
| 2.6711 | 18.0 | 330354 | 3.1688 | 0.4273 |
| 2.657 | 19.0 | 348707 | 3.1738 | 0.4274 |
| 2.6387 | 20.0 | 367060 | 3.1756 | 0.4274 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
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reneeshdenny/ppo-Pyramid_Training | 2023-11-02T19:08:30.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | reneeshdenny | null | null | reneeshdenny/ppo-Pyramid_Training | 0 | 2 | ml-agents | 2023-11-02T19:08:08 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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/
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 teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: reneeshdenny/ppo-Pyramid_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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] |
reneeshdenny/a2c-PandaReachDense-v3 | 2023-11-02T19:32:49.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | reneeshdenny | null | null | reneeshdenny/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-02T19:26:52 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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LoneStriker/openchat_3.5-4.0bpw-h6-exl2 | 2023-11-03T20:11:38.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"arxiv:2309.11235",
"arxiv:2303.08774",
"arxiv:2212.10560",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/openchat_3.5-4.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-11-02T19:40:26 | ---
license: apache-2.0
---
# OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
<p align="center">
<a href="https://openchat.team">Online Demo</a> •
<a href="https://discord.gg/pQjnXvNKHY">Discord</a> •
<a href="https://huggingface.co/openchat">Huggingface</a> •
<a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a>
</p>
**🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥**
**🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖**
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%">
</div>
OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision.
[](https://zenodo.org/badge/latestdoi/645397533)
## Usage
To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command.
Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience.
If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server.
<details>
<summary>Example request (click to expand)</summary>
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_3.5",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
```
Coding Mode
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_3.5",
"condition": "Code",
"messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}]
}'
```
</details>
| Model | Size | Context | Weights | Serving |
|--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
| OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` |
For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below.
<details>
<summary>Conversation templates (click to expand)</summary>
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Coding Mode
tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
```
</details>
## <a id="benchmarks"></a> Benchmarks
| Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K |
|--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
| OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** |
| ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 |
| Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 |
| Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 |
| | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
*: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
**: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks).
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
**Safety**
OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.
## License
Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.
## Citation
```
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
```
## Acknowledgements
We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training.
Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions.
Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
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mmek/ppo-lunarlander-v2_first | 2023-11-02T20:28:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mmek | null | null | mmek/ppo-lunarlander-v2_first | 0 | 2 | stable-baselines3 | 2023-11-02T20:28:20 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.45 +/- 21.70
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
Jack-cpu/Falcon_7_Email_Qlora | 2023-11-02T20:35:09.000Z | [
"peft",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Falcon_7_Email_Qlora | 0 | 2 | peft | 2023-11-02T20:34:51 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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ayman2002rahman/ppo-LunarLander-v2 | 2023-11-02T21:42:42.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ayman2002rahman | null | null | ayman2002rahman/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-02T21:42:18 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.56 +/- 20.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
Jack-cpu/Llama2_7_Email_Qlora | 2023-11-02T22:15:37.000Z | [
"peft",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Llama2_7_Email_Qlora | 0 | 2 | peft | 2023-11-02T22:15:15 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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] |
Jack-cpu/Llama2_7_AIACT_Qlora | 2023-11-02T23:43:13.000Z | [
"peft",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Llama2_7_AIACT_Qlora | 0 | 2 | peft | 2023-11-02T23:42:57 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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viditnaik/hateBERT-finetuned-glue | 2023-11-03T00:42:27.000Z | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | viditnaik | null | null | viditnaik/hateBERT-finetuned-glue | 0 | 2 | transformers | 2023-11-03T00:35:51 | ---
base_model: GroNLP/hateBERT
tags:
- generated_from_keras_callback
model-index:
- name: viditnaik/hateBERT-finetuned-glue
results: []
---
<!-- 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. -->
# viditnaik/hateBERT-finetuned-glue
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.5046
- Validation Loss: 2.9698
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5046 | 2.9698 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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chrisanthony/distilbert-base-uncased-finetuned_emotion | 2023-11-03T19:17:08.000Z | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | chrisanthony | null | null | chrisanthony/distilbert-base-uncased-finetuned_emotion | 0 | 2 | transformers | 2023-11-03T00:46:47 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned_emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9251231117223104
---
<!-- 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-finetuned_emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2259
- Accuracy: 0.925
- F1: 0.9251
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8685 | 1.0 | 250 | 0.3312 | 0.9055 | 0.9043 |
| 0.2531 | 2.0 | 500 | 0.2259 | 0.925 | 0.9251 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
TheBloke/japanese-stablelm-instruct-beta-7B-AWQ | 2023-11-03T12:54:30.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"japanese-stablelm",
"causal-lm",
"ja",
"dataset:kunishou/hh-rlhf-49k-ja",
"dataset:kunishou/databricks-dolly-15k-ja",
"dataset:kunishou/oasst1-89k-ja",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/japanese-stablelm-instruct-beta-7B-AWQ | 0 | 2 | transformers | 2023-11-03T01:04:31 | ---
base_model: stabilityai/japanese-stablelm-instruct-beta-7b
datasets:
- kunishou/hh-rlhf-49k-ja
- kunishou/databricks-dolly-15k-ja
- kunishou/oasst1-89k-ja
inference: false
language:
- ja
license:
- llama2
model_creator: Stability AI
model_name: Japanese StableLM Instruct Beta 7B
model_type: llama
pipeline_tag: text-generation
prompt_template: "<s>[INST] <<SYS>>\n\u3042\u306A\u305F\u306F\u5F79\u7ACB\u3064\u30A2\
\u30B7\u30B9\u30BF\u30F3\u30C8\u3067\u3059\u3002\n<<SYS>>\n\n{prompt} [/INST] \n"
quantized_by: TheBloke
tags:
- japanese-stablelm
- causal-lm
---
<!-- markdownlint-disable MD041 -->
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Japanese StableLM Instruct Beta 7B - AWQ
- Model creator: [Stability AI](https://huggingface.co/stabilityai)
- Original model: [Japanese StableLM Instruct Beta 7B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b)
<!-- description start -->
## Description
This repo contains AWQ model files for [Stability AI's Japanese StableLM Instruct Beta 7B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-7B-GGUF)
* [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Japanese-StableLM-Llama-2-Chat
```
<s>[INST] <<SYS>>
あなたは役立つアシスタントです。
<<SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `['llama2']`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Stability AI's Japanese StableLM Instruct Beta 7B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-7B-AWQ/tree/main) | 4 | 128 | japanese | 4096 | 3.89 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/japanese-stablelm-instruct-beta-7B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `japanese-stablelm-instruct-beta-7B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/japanese-stablelm-instruct-beta-7B-AWQ --quantization awq
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<s>[INST] <<SYS>>
あなたは役立つアシスタントです。
<<SYS>>
{prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/japanese-stablelm-instruct-beta-7B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/japanese-stablelm-instruct-beta-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] <<SYS>>
あなたは役立つアシスタントです。
<<SYS>>
{prompt} [/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using AutoAWQ
### Install the AutoAWQ package
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.1 or later.
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### AutoAWQ example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/japanese-stablelm-instruct-beta-7B-AWQ"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] <<SYS>>
あなたは役立つアシスタントです。
<<SYS>>
{prompt} [/INST]
'''
print("*** Running model.generate:")
token_input = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
token_input,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("LLM output: ", text_output)
"""
# Inference should be possible with transformers pipeline as well in future
# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
"""
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Stability AI's Japanese StableLM Instruct Beta 7B
# Japanese-StableLM-Instruct-Beta-7B
> A cute robot wearing a kimono writes calligraphy with one single brush — [Stable Diffusion XL](https://clipdrop.co/stable-diffusion)
## Model Description
`japanese-stablelm-instruct-beta-7b` is a 7B-parameter decoder-only language model based on [japanese-stablelm-base-beta-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-7b) and further fine tuned on Databricks Dolly-15k, Anthropic HH, and other public data.
This model is also available in a [larger 70b version](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b), or a [faster version with a specialized tokenizer](https://huggingface.co/stabilityai/japanese-stablelm-instruct-ja_vocab-beta-7b).
## Usage
First install additional dependencies in [requirements.txt](./requirements.txt):
```sh
pip install -r requirements.txt
```
Then start generating text with `japanese-stablelm-instruct-beta-7b` by using the following code snippet:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "stabilityai/japanese-stablelm-instruct-beta-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# The next line may need to be modified depending on the environment
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
def build_prompt(user_query, inputs):
sys_msg = "<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n"
p = sys_msg + user_query + "\n\n" + inputs + " [/INST] "
return p
user_inputs = {
"user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
"inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
)
# this is for reproducibility.
# feel free to change to get different result
seed = 23
torch.manual_seed(seed)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=128,
temperature=0.99,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)
```
We suggest playing with different generation config (`top_p`, `repetition_penalty` etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.
## Model Details
* **Model type**: `japanese-stablelm-instruct-beta-7b` model is an auto-regressive language model based on the Llama2 transformer architecture.
* **Language(s)**: Japanese
* **License**: [Llama2 Community License](https://ai.meta.com/llama/license/).
* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
## Training Dataset
The following datasets were used for the instruction training. Note these are Japanese translated versions of the original datasets, shared by [kunishou](https://huggingface.co/kunishou).
- [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)
- [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
- [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja)
## Use and Limitations
### Intended Use
The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use.
### Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
## Authors
This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by [Takuya Akiba](https://huggingface.co/iwiwi) and [Meng Lee](https://huggingface.co/leemeng). The members of the team are as follows:
- [Meng Lee](https://huggingface.co/leemeng)
- [Fujiki Nakamura](https://huggingface.co/fujiki)
- [Makoto Shing](https://huggingface.co/mkshing)
- [Paul McCann](https://huggingface.co/polm-stability)
- [Takuya Akiba](https://huggingface.co/iwiwi)
- [Naoki Orii](https://huggingface.co/mrorii)
## Acknowledgements
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
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LaTarn/re-density-setfit-model | 2023-11-03T01:08:00.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/re-density-setfit-model | 0 | 2 | sentence-transformers | 2023-11-03T01:07:35 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/re-density-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/re-density-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
capablebear/pokemon-lora | 2023-11-03T11:49:40.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | capablebear | null | null | capablebear/pokemon-lora | 0 | 2 | diffusers | 2023-11-03T01:51:02 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - capablebear/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
| 545 | [
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sbanker/ppo-LunarLander-v2 | 2023-11-03T02:46:47.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sbanker | null | null | sbanker/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T02:46:27 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.13 +/- 23.99
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
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