Instructions to use MartinNav/compliantLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MartinNav/compliantLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MartinNav/compliantLLM", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MartinNav/compliantLLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MartinNav/compliantLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MartinNav/compliantLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MartinNav/compliantLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MartinNav/compliantLLM
- SGLang
How to use MartinNav/compliantLLM 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 "MartinNav/compliantLLM" \ --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": "MartinNav/compliantLLM", "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 "MartinNav/compliantLLM" \ --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": "MartinNav/compliantLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MartinNav/compliantLLM with Docker Model Runner:
docker model run hf.co/MartinNav/compliantLLM
| #!/usr/bin/env python | |
| """Run compliantLLM from its Hugging Face repository directory.""" | |
| import argparse | |
| from pathlib import Path | |
| from transformers import AutoModel, AutoTokenizer | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("prompt") | |
| args = parser.parse_args() | |
| repository = Path(__file__).resolve().parent | |
| tokenizer = AutoTokenizer.from_pretrained(repository, trust_remote_code=True) | |
| model = AutoModel.from_pretrained(repository, trust_remote_code=True).eval() | |
| inputs = tokenizer( | |
| args.prompt, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=1024, | |
| ) | |
| output_ids = model.generate(**inputs)[0] | |
| print(model.decode_output(output_ids)) | |
| if __name__ == "__main__": | |
| main() | |