Instructions to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ControlLLM/Llama3.1-8B-OpenMath16-Instruct")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ControlLLM/Llama3.1-8B-OpenMath16-Instruct", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ControlLLM/Llama3.1-8B-OpenMath16-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/Llama3.1-8B-OpenMath16-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct
- SGLang
How to use ControlLLM/Llama3.1-8B-OpenMath16-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/Llama3.1-8B-OpenMath16-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/Llama3.1-8B-OpenMath16-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/Llama3.1-8B-OpenMath16-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/Llama3.1-8B-OpenMath16-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ControlLLM/Llama3.1-8B-OpenMath16-Instruct with Docker Model Runner:
docker model run hf.co/ControlLLM/Llama3.1-8B-OpenMath16-Instruct
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@@ -90,7 +90,7 @@ The plot below highlights the alignment comparison of the model trained with Con
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### Benchmark Results Table
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The table below summarizes the evaluation results across mathematical tasks and original capabilities for various models and training approaches.
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| Llama3.1-8B-Instruct | 23.7 | 50.9 | 85.6 | 52.1 | 83.4 | 29.9 | 72.4 | 46.7 | 60.5 | 56.3 |
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| OpenMath2-Llama3.1 | 38.4 | 64.1 | 90.3 | 64.3 | 45.8 | 1.3 | 4.5 | 19.5 | 12.9 | 38.6 |
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### Benchmark Results Table
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The table below summarizes the evaluation results across mathematical tasks and original capabilities for various models and training approaches.
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| **Model** | **MathH** | **Math** | **GSM8K** | **Math Avg.** | **ARC** | **GPQA** | **MMLU** | **MMLUP** | **Orig. Avg.** | **Overall** |
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| Llama3.1-8B-Instruct | 23.7 | 50.9 | 85.6 | 52.1 | 83.4 | 29.9 | 72.4 | 46.7 | 60.5 | 56.3 |
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| OpenMath2-Llama3.1 | 38.4 | 64.1 | 90.3 | 64.3 | 45.8 | 1.3 | 4.5 | 19.5 | 12.9 | 38.6 |
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