Instructions to use nvidia/Minitron-4B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Minitron-4B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Minitron-4B-Base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/Minitron-4B-Base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nvidia/Minitron-4B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Minitron-4B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Minitron-4B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nvidia/Minitron-4B-Base
- SGLang
How to use nvidia/Minitron-4B-Base 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 "nvidia/Minitron-4B-Base" \ --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": "nvidia/Minitron-4B-Base", "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 "nvidia/Minitron-4B-Base" \ --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": "nvidia/Minitron-4B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nvidia/Minitron-4B-Base with Docker Model Runner:
docker model run hf.co/nvidia/Minitron-4B-Base
Add results preview
Browse files
README.md
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Minitron is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
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## Citation
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If you find our work helpful, please consider citing our paper:
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Minitron is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
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## Evaluation Results
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*5-shot performance.* Language Understanding evaluated using [Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300):
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| Average |
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| :---- |
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| 58.6 |
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*Zero-shot performance.* Evaluated using select datasets from the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) with additions:
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| HellaSwag | Winogrande | GSM8K| ARC-C | XLSum |
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| :------------- | :------------- | :------------- | :------------- | :------------- |
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| 75.0 | 74.0 | 24.1 | 50.9 | 29.5
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*Code generation performance*. Evaluated using [HumanEval](https://github.com/openai/human-eval):
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| p@1, 0-Shot |
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| :------------- |
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| 23.3 |
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Please refer to our [paper](https://arxiv.org/abs/2407.14679) for the full set of results.
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## Citation
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If you find our work helpful, please consider citing our paper:
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