Model Training

Your Data. Your Model.

We fine-tune open-weight Qwen models on your corpus using knowledge distillation. The result: a compact 7B model that speaks your industry's language.

The Distillation Pipeline

1

QA Pair Generation

Our 72B parameter teacher model reads your entire corpus and generates thousands of high-quality question-answer pairs. These aren't random — they cover the key concepts, edge cases, and domain-specific terminology in your documents.

2

Quality Filtering

Each QA pair is scored for accuracy, relevance, and citation quality. Only pairs that meet our quality threshold make it into the training set. Typical retention: 60-70% of generated pairs.

3

Student Training

The filtered QA pairs are used to fine-tune a compact Qwen 7B model using LoRA adapters. Training runs on our own GPU fleet — your data never leaves our infrastructure.

4

Benchmark & Deploy

The fine-tuned model is evaluated against a held-out test set from your corpus. You see accuracy metrics before deployment. Once approved, it replaces the base model for your corpus queries.

Requirements & Pricing

Corpus Requirements

  • ●Minimum 500 documents (recommended: 2,000+)
  • ●Domain-specific content with consistent terminology
  • ●Clean, well-structured text (we handle OCR if needed)
  • ●Corporate or Sovereign plan required

What You Get

  • ●Dedicated 7B model fine-tuned on your data
  • ●Faster inference than the base 72B model
  • ●Domain-specific vocabulary and reasoning
  • ●Benchmark report comparing base vs fine-tuned
  • ●Automatic retraining when your corpus updates

See a fine-tuned model in action.

View Model Showcase