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LLM Compiler 7b
Overview :
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.
Target Users :
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 Cases
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, enabling the selection of the optimal optimization strategy.
Features
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
How to Use
Install necessary libraries, such as transformers.
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Import the AutoTokenizer and pipeline modules.
Load the tokenizer from the pre-trained model using AutoTokenizer.
Set pipeline parameters, including the model, device mapping, and text generation parameters.
Call the pipeline to generate text, passing in the code snippet to be optimized.
Analyze the generated text to obtain optimization suggestions or the converted code.
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