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LLM Compiler 7b Ftd
LLM Compiler-7b-ftd
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LLM Compiler 7b Ftd
AI code generationAI development assistant#Compiler Optimization#Deep Learning#Code Generation#Performance EnhancementStandard PicksOpen Source
Overview :
LLM Compiler-7b-ftd is a large language model developed by Meta, based on Code Llama, which has been fine-tuned for code optimization and compiler inference. It excels in predicting LLVM optimization effects and can perfectly simulate compiler outputs, making it an ideal tool for compiler optimization tasks.
Target Users :
LLM Compiler is primarily targeted at compiler researchers and engineers, as well as developers who need to optimize their code. It enhances program efficiency and performance by providing advanced code optimization suggestions and automated compiler inference.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 46.9K
Use Cases
Use LLM Compiler to optimize the compiler-generated intermediate representation (IR) to reduce the final program size.
Leverage LLM Compiler to predict the best optimization sequence for a specific assembly code, thereby improving code execution efficiency.
Convert complex assembly code into LLVM IR using LLM Compiler for further analysis and optimization.
Features
Predict optimization effects on LLVM assembly code
Generate the optimal optimization sequence to minimize code size
Disassemble assembly code into LLVM IR
Provide services on different model sizes to meet various latency and performance requirements
Optimize code through deep learning
Support compiler researchers and engineers in research and product development
How to Use
1. Install necessary libraries and dependencies, such as transformers.
2. Load the tokenizer from the pre-trained model using AutoTokenizer.
3. Create a text generation pipeline using transformers.pipeline.
4. Provide the code snippet to be optimized as input to the pipeline.
5. Set relevant parameters for text generation, such as do_sample, top_k, temperature, etc.
6. Call the pipeline to generate optimization suggestions or code.
7. Analyze the generated text results and make further adjustments or apply them as needed.
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