Instructions to use openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf")
model = AutoModelForCausalLM.from_pretrained("openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf

SGLang

How to use openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf with Docker Model Runner:
```
docker model run hf.co/openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf
```

🇹🇭 OpenThaiGPT 0.1.0-beta

OpenThaiGPT Version 0.1.0-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
E-mail: kobkrit@iapp.co.th

License

Source Code: License Apache Software License 2.0.
Weight: For research use only (due to the Facebook LLama's Weight LICENSE).
Note that: A commercial use license for OpenThaiGPT 0.1.0 weight will be released later soon!

Code and Weight

Finetune Code: https://github.com/OpenThaiGPT/openthaigpt-finetune-010beta
Inference Code: https://github.com/OpenThaiGPT/openthaigpt
Weight: https://huggingface.co/kobkrit/openthaigpt-0.1.0-beta

openthaigpt
/

openthaigpt-0.1.0-beta-ckpt-hf

🇹🇭 OpenThaiGPT 0.1.0-beta

Support

License

Code and Weight

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Authors

Datasets used to train openthaigpt/openthaigpt-0.1.0-beta-ckpt-hf