Instructions to use Phonsiri/Thai-Legal-Gemma-4B-CPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phonsiri/Thai-Legal-Gemma-4B-CPT with Transformers:

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

pipe = pipeline("text-generation", model="Phonsiri/Thai-Legal-Gemma-4B-CPT")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Phonsiri/Thai-Legal-Gemma-4B-CPT")
model = AutoModelForMultimodalLM.from_pretrained("Phonsiri/Thai-Legal-Gemma-4B-CPT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Phonsiri/Thai-Legal-Gemma-4B-CPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phonsiri/Thai-Legal-Gemma-4B-CPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phonsiri/Thai-Legal-Gemma-4B-CPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Phonsiri/Thai-Legal-Gemma-4B-CPT

SGLang

How to use Phonsiri/Thai-Legal-Gemma-4B-CPT 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 "Phonsiri/Thai-Legal-Gemma-4B-CPT" \
    --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": "Phonsiri/Thai-Legal-Gemma-4B-CPT",
		"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 "Phonsiri/Thai-Legal-Gemma-4B-CPT" \
        --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": "Phonsiri/Thai-Legal-Gemma-4B-CPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Phonsiri/Thai-Legal-Gemma-4B-CPT with Docker Model Runner:
```
docker model run hf.co/Phonsiri/Thai-Legal-Gemma-4B-CPT
```

Thai-Legal-Gemma-4B-CPT / checkpoint-10 /model.safetensors

Phonsiri

Training in progress, step 10, checkpoint

2787a82 verified 4 months ago

Download

History Blame

15.9 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d47860455c52b95974d697e5e48f8ae7d736387f57a145b4daf507b17cf55487
Size of remote file:: 15.9 GB
SHA256:: d75955131280459091805df85614e76c8b1eb0c0903432e883625d3fa82859f4

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.