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requirements.txt
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# Core
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onnxruntime-genai --pre
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# onnxruntime>=1.17.0 # Generally not needed to list explicitly
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#
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# Core ML/LLM Libraries
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# Note: torch should ideally be installed separately following PyTorch official instructions
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# based on your CUDA version (if using GPU) for optimal performance.
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# See: https://pytorch.org/get-started/locally/
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torch
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# Hugging Face libraries
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transformers>=4.38.0 # Gemma requires a recent version
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accelerate>=0.25.0 # Required by transformers and unsloth for optimized loading
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datasets # Often useful, though not strictly required by the script itself
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# Quantization library (needed for load_in_4bit=True)
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bitsandbytes>=0.41.3 # Check Unsloth docs for compatible versions if issues arise
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# Interface library
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gradio>=4.0.0 # Using a recent version
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# --- IMPORTANT ---
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# Unsloth needs to be installed separately following their specific instructions,
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# as it often involves non-standard pip commands (e.g., installing from GitHub)
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# or conda commands depending on your setup (CPU, CUDA version, ROCm).
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#
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# Example (check Unsloth's GitHub for the correct command for *your* system):
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# pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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#
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# Or via Conda (check Unsloth docs):
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# conda install "unsloth::unsloth[conda-new]" # Example
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#
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# Because Unsloth is not listed here, you MUST install it manually *before*
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# trying to use this requirements.txt file or running the script.
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# Other common dependencies that might be implicitly needed
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Jinja2
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packaging
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# sentencepiece # Often used by tokenizers, good to include
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# protobuf # Sometimes needed by specific models/tokenizers
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