Updated Readme

README.md

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-license: mit
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+# 🇹🇭 OpenThaiGPT 1.0.0-beta
+<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2Fb8eiMDaqiEQL6ahbAY0h%2Fimage.png?alt=media&token=6fce78fd-2cca-4c0a-9648-bd5518e644ce" width="200px">
+
+OpenThaiGPT Version 1.0.0-beta is a 7B-parameter LLaMA model finetuned to follow Thai translated instructions below and makes use of the Huggingface LLaMA implementation. 
+
+## Support
+- Official website: https://openthaigpt.aieat.or.th
+- Facebook page: https://web.facebook.com/groups/openthaigpt
+- A Discord server for discussion and support [here](https://discord.gg/rUTp6dfVUF)
+- E-mail: kobkrit@iapp.co.th
+
+## License
+- **Source Code**: Apache Software License 2.0.<br>
+- **Weight**: For research use only (due to the Facebook LLama's Weight LICENSE).<br>
+- <i>Note that: A commercial use license for OpenThaiGPT 0.1.0 weight will be released later soon!</i>
+
+## Code and Weight
+
+- **Libary Code**: https://github.com/OpenThaiGPT/openthaigpt<br>
+- **Finetune Code**: https://github.com/OpenThaiGPT/openthaigpt-finetune-010beta<br>
+- **Weight**: https://huggingface.co/kobkrit/openthaigpt-0.1.0-beta
+
+## Sponsors
+Pantip.com, ThaiSC<br>
+<table>
+<tr><td>
+<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2FiWjRxBQgo0HUDcpZKf6A%2Fimage.png?alt=media&token=4fef4517-0b4d-46d6-a5e3-25c30c8137a6" width="100px"></td><td>
+<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2Ft96uNUI71mAFwkXUtxQt%2Fimage.png?alt=media&token=f8057c0c-5c5f-41ac-bb4b-ad02ee3d4dc2" width="100px"></td>
+</tr><table>
+
+### Powered by
+OpenThaiGPT Volunteers, Artificial Intelligence Entrepreneur Association of Thailand (AIEAT), and Artificial Intelligence Association of Thailand (AIAT)
+
+<table>
+<tr>
+<td>
+<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2F6yWPXxdoW76a4UBsM8lw%2Fimage.png?alt=media&token=1006ee8e-5327-4bc0-b9a9-a02e93b0c032" width="100px"></td><td><img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2FBwsmSovEIhW9AEOlHTFU%2Fimage.png?alt=media&token=5b550289-e9e2-44b3-bb8f-d3057d74f247" width="100px"></td></tr><table>
+
+### Authors
+Kobkrit Viriyayudhakorn (kobkrit@iapp.co.th), Sumeth Yuenyong (sumeth.yue@mahidol.edu) and Thaweewat Ruksujarit (thaweewr@scg.com).
+
+<i>Disclaimer: Provided responses are not guaranteed.</i>
+
+### Local Setup
+
+1. Install dependencies
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+1. If bitsandbytes doesn't work, [install it from source.](https://github.com/TimDettmers/bitsandbytes/blob/main/compile_from_source.md) Windows users can follow [these instructions](https://github.com/tloen/alpaca-lora/issues/17).
+
+### Training (`finetune.py`)
+
+This file contains a straightforward application of PEFT to the LLaMA model,
+as well as some code related to prompt construction and tokenization.
+PRs adapting this code to support larger models are always welcome.
+
+Example usage:
+
+```bash
+python finetune.py \
+    --base_model 'decapoda-research/llama-7b-hf' \
+    --data_path 'Thaweewat/alpaca-cleaned-52k-th' \
+    --output_dir './openthaigpt-010-beta'
+```
+
+We can also tweak our hyperparameters:
+
+```bash
+python finetune.py \
+    --base_model 'decapoda-research/llama-7b-hf' \
+    --data_path 'Thaweewat/alpaca-cleaned-52k-th' \
+    --output_dir './openthaigpt-010-beta' \
+    --batch_size 128 \
+    --micro_batch_size 4 \
+    --num_epochs 3 \
+    --learning_rate 1e-4 \
+    --cutoff_len 512 \
+    --val_set_size 2000 \
+    --lora_r 8 \
+    --lora_alpha 16 \
+    --lora_dropout 0.05 \
+    --lora_target_modules '[q_proj,v_proj]' \
+    --train_on_inputs \
+    --group_by_length
+```
+
+### Inference (`generate.py`)
+
+This file reads the foundation model from the Hugging Face model hub and the LoRA weights from `kobkrit/openthaigpt-0.1.0-beta`, and runs a Gradio interface for inference on a specified input. Users should treat this as example code for the use of the model, and modify it as needed.
+
+Example usage:
+
+```bash
+python generate.py \
+    --load_8bit \
+    --base_model 'decapoda-research/llama-7b-hf' \
+    --lora_weights 'kobkrit/openthaigpt-0.1.0-beta'
+```
+
+### Official weights
+
+The most recent "official" OpenThaiGPT 0.1.0-beta adapter available at [`kobkrit/openthaigpt-0.1.0-beta`](https://huggingface.co/kobkrit/openthaigpt-0.1.0-beta) was trained on May 13 with the following command:
+
+```bash
+python finetune.py \
+    --base_model='decapoda-research/llama-7b-hf' \
+    --data_path '../datasets/cleaned' \
+    --num_epochs=3 \
+    --cutoff_len=2048 \
+    --group_by_length \
+    --output_dir='./openthaigpt-010-beta' \
+    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
+    --lora_r=64 \
+    --batch_size=64 \
+    --micro_batch_size=4
+```
+
+### Checkpoint export (`export_*_checkpoint.py`)
+
+These files contain scripts that merge the LoRA weights back into the base model
+for export to Hugging Face format and to PyTorch `state_dicts`.
+They should help users
+who want to run inference in projects like [llama.cpp](https://github.com/ggerganov/llama.cpp)
+or [alpaca.cpp](https://github.com/antimatter15/alpaca.cpp).
+
+### Docker Setup & Inference
+
+1. Build the container image:
+
+```bash
+docker build -t openthaigpt-finetune-010beta .
+```
+
+2. Run the container (you can also use `finetune.py` and all of its parameters as shown above for training):
+
+```bash
+docker run --gpus=all --shm-size 64g -p 7860:7860 -v ${HOME}/.cache:/root/.cache --rm openthaigpt-finetune-010beta generate.py \
+    --load_8bit \
+    --base_model 'decapoda-research/llama-7b-hf' \
+    --lora_weights 'kobkrit/openthaigpt-0.1.0-beta'
+```
+
+3. Open `https://localhost:7860` in the browser
+
+### Docker Compose Setup & Inference
+
+1. (optional) Change desired model and weights under `environment` in the `docker-compose.yml`
+
+2. Build and run the container
+
+```bash
+docker-compose up -d --build
+```
+
+3. Open `https://localhost:7860` in the browser
+
+4. See logs:
+
+```bash
+docker-compose logs -f
+```
+
+5. Clean everything up:
+
+```bash
+docker-compose down --volumes --rmi all
+```
+