Fine-tuning for specialized needs

Model tuning as engineering

Fine-tuning is a trade-off between cost, speed, and accuracy. We don't just jump to the most complex solution. We start with the simplest intervention—like prompt engineering or retrieval—and only move to LoRA or full fine-tuning when there's evidence that it's necessary.

• Data preparation: We handle the heavy lifting of cleaning data, removing sensitive information, and building high-quality test sets.
• Efficient training: We use LoRA and QLoRA on open models like Llama and Mistral to get high performance without needing massive compute resources.
• Tone and behavior: We use techniques like DPO to ensure the model follows your brand's specific tone and avoids unwanted behaviors.
• Owned assets: You get the final model weights and training code, so you're not locked into a specific provider's API.

Technical standards

Every training run is tracked and every dataset is verified. We provide model weights and a testing framework you can run yourself.

• Reproducibility: We document every step and dependency so you can recreate our results.
• Evaluation: We test models against specific tasks and general benchmarks to ensure no loss in overall capability.
• Full ownership: All final artifacts are yours to deploy on your own servers.