Instructions to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct
- SGLang
How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct 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 "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct" \ --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": "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct", "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 "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct" \ --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": "ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct with Docker Model Runner:
docker model run hf.co/ControlLLM/Control-LLM-Llama3.1-8B-Math16-Instruct
Commit History
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Improve model card (#1)
Verified signature- Improve model card (b33953c2d3b75cfe958fdb2e4cf782a9ac1ae120) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>677843e -
link the open source code
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Add paper link
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update config
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update explanation of benchmark result table
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update readme with benchmark result
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upload model and model card
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initial commit
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