Instructions to use xaskasdf/llm-compiler-13b-ftd-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use xaskasdf/llm-compiler-13b-ftd-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="xaskasdf/llm-compiler-13b-ftd-gguf", filename="llm-compiler-13b-ftd-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use xaskasdf/llm-compiler-13b-ftd-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16 # Run inference directly in the terminal: llama-cli -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16 # Run inference directly in the terminal: llama-cli -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf xaskasdf/llm-compiler-13b-ftd-gguf:F16
Use Docker
docker model run hf.co/xaskasdf/llm-compiler-13b-ftd-gguf:F16
- LM Studio
- Jan
- Ollama
How to use xaskasdf/llm-compiler-13b-ftd-gguf with Ollama:
ollama run hf.co/xaskasdf/llm-compiler-13b-ftd-gguf:F16
- Unsloth Studio
How to use xaskasdf/llm-compiler-13b-ftd-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xaskasdf/llm-compiler-13b-ftd-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xaskasdf/llm-compiler-13b-ftd-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for xaskasdf/llm-compiler-13b-ftd-gguf to start chatting
- Docker Model Runner
How to use xaskasdf/llm-compiler-13b-ftd-gguf with Docker Model Runner:
docker model run hf.co/xaskasdf/llm-compiler-13b-ftd-gguf:F16
- Lemonade
How to use xaskasdf/llm-compiler-13b-ftd-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull xaskasdf/llm-compiler-13b-ftd-gguf:F16
Run and chat with the model
lemonade run user.llm-compiler-13b-ftd-gguf-F16
List all available models
lemonade list
Model Card: LLM Compiler 13B FTD GGUF
This is a GGUF conversion and quantization of Meta's LLM Compiler 13B FTD model, optimized for code and compiler tasks.
Model Details
- Developed by: Meta
- Model type: LLM Compiler 13B FTD (Fine-tuned for code size and disassembly)
- Language(s): English, relevant programming languages, LLVM IR, x86_64 assembly, and ARM assembly
- License: Meta Large Language Model Compiler (LLM Compiler) License Agreement
- Model Architecture: Auto-regressive language model using an optimized transformer architecture
- Conversion: GGUF format conversion and quantization performed using llama.cpp build 3263 (26a39bbd) on Windows 11
- Compiler used: MSVC 19.37.32824.0 for x64
Model Description
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.
The model is fine-tuned for:
- Predicting optimal optimization passes for LLVM's
optto minimize code size - Generating LLVM IR from x86_64 or ARM assembly code
Intended Use
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:
- Emulating compiler transformations
- Optimizing code for size
- Disassembling and decompiling code
Limitations and Ethical Considerations
- The model's performance may vary from the original due to quantization.
- It should not be used for generating or manipulating code in ways that could lead to harmful or malicious software.
- The model may produce unexpected or inaccurate results in some cases.
- Users should perform their own safety testing and tuning for specific applications.
How to Use
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.
Example prompt formats can be found in the llm_compiler_demo.py file from the original repository.
License and Use Restrictions
This model is subject to multiple licenses:
The original model is licensed under the Meta Large Language Model Compiler (LLM Compiler) License Agreement. Key points of this license include:
- 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.
- Any distribution must include a copy of the license agreement and display "Built with LLM Compiler" prominently.
- Compliance with applicable laws and the Acceptable Use Policy for Llama Materials is required.
- The model cannot be used to improve other large language models.
- Special licensing is required for products or services with over 700 million monthly active users.
The GGUF conversion is subject to the MIT License, as it uses llama.cpp for the conversion process.
Usage of the model must comply with the Acceptable Use Policy for Llama Materials.
Commercial use may require additional licensing from Meta. Users are responsible for ensuring their use complies with all applicable licenses and policies.
For full license details and use restrictions, please refer to:
- LLM Compiler License Agreement
- Acceptable Use Policy
- MIT License (for the GGUF conversion process)
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.
Additional Information
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"
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.
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