%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Minference 1.0
Overview :
MInference 1.0 is a sparse computation method aimed at accelerating the pre-fill stage of long sequence processing. It implements a dynamic sparse attention method for long-context large language models (LLMs) by identifying three unique patterns in the long context attention matrix, accelerating the pre-fill stage for 1M token prompts while maintaining the capabilities of LLMs, especially retrieval capabilities.
Target Users :
MInference 1.0 is designed for researchers and developers working with large datasets and long-context information, particularly in natural language processing and machine learning. By optimizing the use of computational resources, it enables large language models to process and generate text faster, making it suitable for applications requiring efficient text generation and retrieval capabilities.
Use Cases
In question answering (QA) tasks, MInference 1.0 can quickly retrieve and generate accurate answers.
In programming tasks, MInference 1.0 can assist developers in quickly writing and understanding code.
In multi-hop QA tasks, MInference 1.0 can process complex context information and provide coherent answers.
Features
Dynamic sparse attention method, accelerating the pre-fill stage for long-context LLMs, boosting processing speed up to 10 times.
Divides dynamic sparse attention into three modes: A-shape, Vertical-Slash, and Block-Sparse, and designs Kernel-Aware Sparse Pattern Search algorithm to find the optimal head mode.
Introduces online approximation methods and optimized GPU kernels to accelerate LLM inference with minimal overhead.
Presents a best-practice inference code library, enabling 1M token pre-fill inference for LLaMA-style models on a single A100.
Evaluates MInference on multiple benchmark tests including InfiniteBench, RULER, PG-19, and Needle in a Haystack to assess the practical context processing capabilities of LLMs.
Demonstrates the performance of the proposed three attention modes via micro-benchmark tests, comparing with FlashAttention.
Tests MInference on different models and methods, including evaluating the performance on different context windows and key information positions within prompts in the Needle in a Haystack task.
How to Use
Step 1: Visit the online demo of MInference 1.0 or download the code.
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%230080ff;%20}%20%3c/style%3e%3c/defs%3e%3cpath%20class='st0'%20d='M16.2,11.1c.4.5.4,1.2,0,1.8l-4.7,7.1h-3.8l5.3-8L7.6,4h3.8l4.7,7.1Z'/%3e%3c/svg%3e)
Step 2: Configure the required environment and dependencies according to the documentation.
Step 3: Load your long-context data or model.
Step 4: Use MInference 1.0's API or command-line tools to perform pre-fill processing on the data.
Step 5: Run the optimized inference process and observe the processing speed and result quality.
Step 6: Adjust parameters as needed to achieve optimal performance and accuracy.
Featured AI Tools
Tensorpool
TensorPool is a cloud GPU platform dedicated to simplifying machine learning model training. It provides an intuitive command-line interface (CLI) enabling users to easily describe tasks and automate GPU orchestration and execution. Core TensorPool technology includes intelligent Spot instance recovery, instantly resuming jobs interrupted by preemptible instance termination, combining the cost advantages of Spot instances with the reliability of on-demand instances. Furthermore, TensorPool utilizes real-time multi-cloud analysis to select the cheapest GPU options, ensuring users only pay for actual execution time, eliminating costs associated with idle machines. TensorPool aims to accelerate machine learning engineering by eliminating the extensive cloud provider configuration overhead. It offers personal and enterprise plans; personal plans include a $5 weekly credit, while enterprise plans provide enhanced support and features.
Model Training and Deployment
307.5K
Scireviewhub
SciReviewHub is an AI-powered tool designed to accelerate scientific writing and literature reviews. We leverage AI technology to quickly filter relevant papers based on your research goals and synthesize the most pertinent information into easily understandable and readily usable literature reviews. Through our platform, you can enhance your research efficiency, expedite publication timelines, and achieve breakthroughs in your field. Join SciReviewHub and reshape the future of scientific writing!
Research Tools
285.4K