- Added comprehensive local model fine-tuning guide - Created benchmarking script for inference performance - Added training data collection script for merged PRs - Documented current stack (Ollama + llama.cpp + Hermes 4) - Provided quantization options and best practices - Included troubleshooting and monitoring guidance Addresses issue #486 recommendations: ✓ Documented local model stack for reproducibility ✓ Created benchmarking tools for inference latency ✓ Provided training data collection pipeline ✓ Documented quantization options for faster inference ✓ Included fine-tuning pipeline documentation
26 lines
384 B
Plaintext
26 lines
384 B
Plaintext
# Local Model Fine-Tuning Dependencies
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# Core dependencies
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requests>=2.31.0
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numpy>=1.24.0
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pandas>=2.0.0
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# For benchmarking
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matplotlib>=3.7.0
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seaborn>=0.12.0
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# For data processing
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jsonlines>=3.1.0
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tqdm>=4.65.0
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# Optional: for advanced fine-tuning
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# torch>=2.0.0
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# transformers>=4.30.0
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# peft>=0.4.0
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# datasets>=2.14.0
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# Development tools
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pytest>=7.4.0
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black>=23.0.0
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flake8>=6.0.0
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