Instructions to use eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ") model = AutoModelForCausalLM.from_pretrained("eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ
- SGLang
How to use eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ 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 "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ" \ --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": "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ", "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 "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ" \ --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": "eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ with Docker Model Runner:
docker model run hf.co/eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ
- Xet hash:
- 69ce049c995cccfd98d730e13885ab1616f0407b33b2c17493c11130ad28ba5e
- Size of remote file:
- 1.26 MB
- SHA256:
- 7c62ac02fb4207c22252e08daf147266b8b602ba16ae3a11112fbb40fbd8ef73
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