Domain-tuned models,
in your bucket.
End-to-end fine-tuning — data, training, evaluation, deployment. LoRA, QLoRA, DoRA, or full fine-tune. We integrate with HF TRL, Together, Modal, AWS Bedrock, GCP and on-prem.
Built for production, not just demos.
- LoRA / QLoRA / DoRA / full-parameter fine-tuning
- Distillation from larger to smaller models
- Model merge (SLERP, DARE, TIES, FrankenMerge)
- Multi-task & multi-domain checkpoints
- Continual learning + catastrophic-forgetting mitigation
- Quantization (GGUF, AWQ, GPTQ, EXL2)
- Eval harness integrations (HELM, MMLU, MT-Bench, custom)
- Cluster choice — HF, Together, Modal, AWS, GCP, on-prem
How a typical engagement runs.
Discover
Define target tasks, evaluation criteria, base model and adapter strategy.
Data
Assemble SFT + RLHF data via our pods or your existing corpus.
Train
Run on your cluster or ours; monitor loss curves, eval slices, and ablation reports.
Evaluate
Multi-rubric eval suite plus your task-specific benchmarks + side-by-side vs baseline.
Ship
Quantized checkpoint + model card + deployment guide for vLLM / TGI / your stack.
What you get in your bucket.
Questions, answered.
Which base models do you support?
Llama 3 (8B/70B/405B), Mistral, Qwen 2, Gemma, DeepSeek, Phi-3, Yi, plus private models you hold. Closed APIs (GPT/Claude) supported via their tuning endpoints where available.
LoRA or full fine-tune?
Default to LoRA/QLoRA for cost + quick iteration. We escalate to DoRA or full-parameter when accuracy ceiling isn't met or domain drift is large.
Where does training run?
Your cluster, our cluster, or a hosted provider (Together, Modal, RunPod). We bring our own kit for air-gapped on-prem when required.
How do you avoid catastrophic forgetting?
Replay of base-task data + KL penalty + adapter merging + multi-task SFT mixes. We publish a forgetting score in the eval report.
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