Local Model Fine-Tuning: Reducing Costs 10x

Why Local Models?

1. Cost: API calls add up. Local = marginal cost near zero.
2. Privacy: Sensitive data stays on hardware.
3. Speed: No network latency.
4. Control: No rate limits, no policy changes.

Research Questions

1. What tasks can local models handle?

   • Sentiment analysis
   • Classification
   • Summarization
   • Simple Q&A

2. What requires frontier models?

   • Complex reasoning
   • Novel code generation
   • Multi-step planning

3. Fine-tuning approaches

   • LoRA adapters
   • QLoRA for memory efficiency
   • Dataset curation

4. Hardware requirements

   • Mac Mini M4 capabilities
   • Ollama vs vLLM vs llama.cpp
   • Quantization tradeoffs

Models to Evaluate

• Qwen 2.5 (7B, 14B, 32B, 72B)
• Llama 3.1 (8B, 70B)
• Mistral/Mixtral
• DeepSeek

Experiments

1. Benchmark local vs API for specific tasks
2. Find breakeven point (when does local save money?)
3. Test fine-tuning on our data