Here's why Meta LLM Compiler achieves state-of-the-art results in optimizing code size and disassembly tasks:
1. **Building on Code Llama:** Meta LLM Compiler takes the foundation of Code Llama, a large language model (LLM) specifically trained for code understanding. This provides a strong base for comprehending code structure and functionality.
2. **Enhanced Code Optimization and Compiler Reasoning:** The compiler goes beyond Code Llama's capabilities by being fine-tuned for tasks related to code optimization and understanding compiler actions. This specialization allows it to reason about the impact of optimizations and make informed suggestions.
3. **Focus on Intermediate Representations (IRs) and Assembly:** The model is trained on compiler intermediate representations (IRs) and assembly language. IRs are a format that compilers use internally to represent code, and assembly is a low-level language directly understandable by processors. By being familiar with these representations, the compiler can analyze code structure more effectively and identify optimization opportunities.
4. **Openly Available Pre-trained Models:** Meta provides a suite of pre-trained LLM Compiler models. These models are ready to use, allowing developers to leverage their capabilities without the need for extensive training from scratch. This accessibility fosters further research and development in code optimization.
In summary, Meta LLM Compiler combines the strengths of Code Llama with additional training focused on compiler tasks and code representations. This specialization allows it to outperform previous models in optimizing code size and performing disassembly tasks.
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Today we’re releasing Meta LLM Compiler, a family of models built on Meta Code Llama with additional code optimization and compiler capabilities. The models achieve state-of-the-art results on optimization of code size and disassembly tasks.
Hugging Face repo ➡️ https://go.fb.me/tdd3dw
Research paper ➡️ https://go.fb.me/hvfnam
LLM Compiler can emulate the compiler, predict optimal passes for code size, and disassemble code. It can be fine-tuned for new optimizations and compiler tasks. This work shows that AI is learning to optimize code and can assist compiler experts in identifying opportunities to apply optimizations. We believe this work could have an impact ranging from use in optimization for individual developer environments to inclusion in a compiler such as LLVM.
We’re releasing LLM Compiler 7B & 13B models under a permissive license for both research and commercial use in the hopes of making it easier for developers and researchers alike to leverage this in their work and carry forward new research in this highly impactful space.
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