MInference
M
Minference
Overview :
MInference is an inference acceleration framework for long context large language models (LLMs). It leverages the dynamic sparse characteristics of LLMs' attention mechanisms, significantly enhancing the speed of pre-filling through static pattern recognition and online sparse index approximation computation. It achieves a tenfold increase in processing 1M contexts on a single A100 GPU while maintaining inference accuracy.
Target Users :
MInference is primarily designed for researchers and developers who need to handle large-scale language model inference tasks, particularly those who require efficient inference on limited hardware resources.
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How to Use
1. Install the necessary dependencies, including Torch and FlashAttention-2.
2. Use pip to install MInference.
3. Import the MInference module and apply it to the model based on the model framework used (such as Hugging Face's transformers or vLLM).
4. Patch the model using the MInference module to exploit the dynamic sparse attention features.
5. Run the inference task and enjoy the performance improvement brought by the acceleration.
6. Refer to the examples and experiments provided by MInference to further explore and optimize the usage.
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