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# Model Card: LLM Compiler 13B FTD GGUF |
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This is a GGUF conversion and quantization of Meta's LLM Compiler 13B FTD model, optimized for code and compiler tasks. |
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## Model Details |
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- **Developed by:** Meta |
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- **Model type:** LLM Compiler 13B FTD (Fine-tuned for code size and disassembly) |
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- **Language(s):** English, relevant programming languages, LLVM IR, x86_64 assembly, and ARM assembly |
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- **License:** Meta Large Language Model Compiler (LLM Compiler) License Agreement |
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- **Model Architecture:** Auto-regressive language model using an optimized transformer architecture |
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- **Conversion:** GGUF format conversion and quantization performed using llama.cpp build 3263 (26a39bbd) on Windows 11 |
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- **Compiler used:** MSVC 19.37.32824.0 for x64 |
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## Model Description |
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LLM Compiler 13B FTD is part of the LLM Compiler family developed by Meta, designed specifically for code optimization tasks. This GGUF version is a converted and quantized variant of the original model, optimized for use with llama.cpp. |
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The model is fine-tuned for: |
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1. Predicting optimal optimization passes for LLVM's `opt` to minimize code size |
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2. Generating LLVM IR from x86_64 or ARM assembly code |
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## Intended Use |
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This model is intended for commercial and research use in tasks related to compiler optimization, code analysis, and transformation. It excels in tasks such as: |
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- Emulating compiler transformations |
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- Optimizing code for size |
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- Disassembling and decompiling code |
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## Limitations and Ethical Considerations |
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- The model's performance may vary from the original due to quantization. |
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- It should not be used for generating or manipulating code in ways that could lead to harmful or malicious software. |
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- The model may produce unexpected or inaccurate results in some cases. |
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- Users should perform their own safety testing and tuning for specific applications. |
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## How to Use |
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To use this model, you'll need to use llama.cpp or compatible software that can work with GGUF format models. Refer to the llama.cpp documentation for specific usage instructions. |
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Example prompt formats can be found in the `llm_compiler_demo.py` file from the original repository. |
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## License and Use Restrictions |
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This model is subject to multiple licenses: |
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1. The original model is licensed under the Meta Large Language Model Compiler (LLM Compiler) License Agreement. Key points of this license include: |
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- Non-exclusive, worldwide, non-transferable and royalty-free limited license to use, reproduce, distribute, copy, create derivative works of, and make modifications to the LLM Compiler Materials. |
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- Any distribution must include a copy of the license agreement and display "Built with LLM Compiler" prominently. |
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- Compliance with applicable laws and the Acceptable Use Policy for Llama Materials is required. |
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- The model cannot be used to improve other large language models. |
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- Special licensing is required for products or services with over 700 million monthly active users. |
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2. The GGUF conversion is subject to the MIT License, as it uses llama.cpp for the conversion process. |
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3. Usage of the model must comply with the Acceptable Use Policy for Llama Materials. |
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Commercial use may require additional licensing from Meta. Users are responsible for ensuring their use complies with all applicable licenses and policies. |
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For full license details and use restrictions, please refer to: |
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- [LLM Compiler License Agreement](https://huggingface.co/facebook/llm-compiler-13b-ftd/blob/main/LICENSE.pdf) |
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- [Acceptable Use Policy](https://llama.meta.com/llama3/use-policy) |
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- [MIT License](https://opensource.org/licenses/MIT) (for the GGUF conversion process) |
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It is strongly recommended to review the full license text and consult with legal counsel if you have any questions about your specific use case. |
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## Additional Information |
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For more details on the original model's performance, training process, and ethical considerations, please refer to the research paper: "Meta Large Language Model Compiler: Foundation Models of Compiler Optimization" |
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This GGUF version was created to enable efficient use with llama.cpp and related projects. Performance may differ from the original model due to quantization and format conversion. |
