Model Distillation & Fine-Tuning

The Challenge with Mega-LLMs

Using GPT-4 for internal document routing or structured data extraction is like using a supercomputer to run a pocket calculator. It’s slow, wildly expensive, and exposes your proprietary data to third-party endpoints.

Our Approach: Distillation

We extract the “knowledge” of frontier models and compress it into smaller, heavily optimized open-weights models (like Llama 3 8B, Mistral, or Phi-3) using a teacher-student distillation pipeline.

Engagement Milestones

1. Synthetic Data Generation: We use frontier models to generate millions of high-quality, domain-specific Q&A pairs and reasoning traces based on your private corpus.
2. LoRA Fine-Tuning: We apply Low-Rank Adaptation to inject this structured knowledge into a 7B-14B parameter model.
3. Quantization: We quantize the model to 4-bit or 8-bit precision (GGUF/AWQ) to drastically reduce VRAM requirements without sacrificing accuracy.
4. Evaluation: Rigorous benchmarking against the original teacher model on your specific eval sets.

The Result

An SLM that fits on a single commodity GPU, runs at 100+ tokens per second, costs pennies per million tokens, and matches or beats GPT-4 on your specific domain tasks.