LLM Compiler-7b

An advanced large language model for code optimization and compiler inference.

CommonProductProgrammingCode OptimizationCompiler Inference

LLM Compiler-7b is a large language model focused on code optimization and compiler inference, developed by Meta. Based on the Code Llama model, it utilizes deep learning to optimize code, supporting understanding of compiler intermediate representations, assembly language, and optimized code. This model demonstrates excellent performance in reducing code size and in reverse compilation from assembly to compiler intermediate representations, serving as a powerful tool for compiler researchers and engineers.

Predict the impact of LLVM optimization on code size
Generate the optimal sequence of optimizations that minimizes code size
Generate LLVM IR from x86_64 or ARM assembly code
7B parameter model offering low latency service on a single GPU
13B parameter model providing the best results
Adhere to Meta's license and acceptable use policies

LLM Compiler is primarily aimed at compiler researchers and engineers who need to optimize code for improved program efficiency and reduced program size. This model can help them quickly find the best optimization solutions
enhancing the development experience.

Use LLM Compiler to optimize compiler-generated intermediate code
reducing the size of the final program.
Leverage LLM Compiler's reverse compilation capabilities to convert assembly code into LLVM IR for further analysis and optimization.
During development
use LLM Compiler to predict the specific impact of different optimization options on code size

Install necessary libraries
such as transformers.
Import the AutoTokenizer and pipeline modules.
Load the tokenizer from the pre-trained model using AutoTokenizer.
Set pipeline parameters

Visit

LLM Compiler-7b Visit Over Time

Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

LLM Compiler-7b Visit Trend

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LLM Compiler-7b

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