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
File size: 771 Bytes
4689b4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | #!/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()
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