A domain-tuned LLM in 6 weeks.
We assemble the SFT and RLHF data, run the training, evaluate against your tasks, and ship the checkpoint — all under one engagement.
What you walk away with.
- Fine-tuned model checkpoint with full eval report
- Curated SFT + RLHF datasets ready for re-use
- Side-by-side win-rate vs your baseline
- Inference benchmark + serving guide
- Drift monitoring + retraining playbook
- Optional Natton-hosted serving
How an engagement runs.
Scope
Pick base model, target tasks, eval criteria and infra.
Data
Curate SFT + RLHF data via our pods or your corpus.
Train
LoRA / QLoRA / DoRA / full fine-tune on HF / Together / your cluster.
Evaluate
Multi-rubric human eval + your benchmarks + side-by-side baseline.
Ship
Quantized checkpoint, model card, deployment guide.
Questions, answered.
Which base models?
Llama 3, Mistral, Qwen, Gemma, DeepSeek, Phi — or private models you hold.
Where does training run?
Your cluster, ours, or a hosted provider (Together, Modal, RunPod). Air-gapped on-prem supported.
What's the typical cost?
Pilot from ~$25k for SFT-only on a 7-13B base; production engagements scale with data + GPU hours. Quote in 24h.
How do you avoid forgetting?
Base-task replay + KL penalty + multi-task mixes + adapter merging. We publish a forgetting score.
Ready to build
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