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LLM Compiler 13b
Overview :
Meta Large Language Model Compiler (LLM Compiler-13b), built upon Code Llama, is an advanced large language model specializing in code optimization and compiler inference. It demonstrates superior understanding of compiler optimization tasks compared to existing publicly available large language models, capable of accurately simulating compiler outputs 20% faster. LLM Compiler offers two model sizes: 7B and 13B parameters, each trained to address different service and latency requirements. This model is free to use for both research and commercial purposes, aiming to support compiler researchers and engineers and inspire the development of innovative tools.
Target Users :
LLM Compiler is designed for compiler researchers, engineers, and developers looking to improve code efficiency and reduce application size. This technology enables them to optimize code automatically, reducing manual optimization efforts while enhancing the accuracy and efficiency of the compiler's output.
Use Cases
Use LLM Compiler to optimize code in large software projects, reducing the final application size.
During compiler development, utilize LLM Compiler to predict the impact of different optimization strategies on code performance.
In the educational field, serve as a teaching tool to help students understand the principles and effects of compiler optimization.
Features
Predict the impact of LLVM optimization on code size
Generate an optimized pass list that minimizes code size
Generate LLVM IR from x86_64 or ARM assembly code
Implement high-fidelity code simulation for compiler optimization tasks
Provide 7B and 13B parameter models to meet diverse latency needs
Optimize code through deep learning, enhancing program efficiency and reducing application size
How to Use
Install necessary libraries and dependencies, such as transformers.
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Download the required LLM Compiler model from Hugging Face.
Use AutoTokenizer to load the tokenizer from the pre-trained model.
Set up transformers.pipeline for text generation, configuring appropriate parameters.
Input the code snippet to be optimized through the pipeline to obtain the model's generated optimization suggestions.
Adjust and optimize the code based on the model's output results.
Test the optimized code to ensure the optimization effect meets expectations.
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