Instructions to use openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml with Transformers:

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

pipe = pipeline("text-generation", model="openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml"
# 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-1.0.0-alpha-7b-chat-ggml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml

SGLang

How to use openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml 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-1.0.0-alpha-7b-chat-ggml" \
    --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-1.0.0-alpha-7b-chat-ggml",
		"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-1.0.0-alpha-7b-chat-ggml" \
        --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-1.0.0-alpha-7b-chat-ggml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml with Docker Model Runner:
```
docker model run hf.co/openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml
```

🇹🇭 OpenThaiGPT 1.0.0-alpha

OpenThaiGPT Version 1.0.0-alpha is the first Thai implementation of a 7B-parameter LLaMA v2 Chat model finetuned to follow Thai translated instructions below and makes use of the Huggingface LLaMA implementation.

---- GGML Format of OpenThaiGPT 1.0.0-alpha ----

Upgrade from OpenThaiGPT 0.1.0-beta

Using Facebook LLama v2 model 7b chat as a base model which is pretrained on over 2 trillion token.
Context Length is upgrade from 2048 token to 4096 token
Allow research and commerical use.a