Generic foundation models are impressive out of the box, but they don't know your product catalog, your legal clauses, your medical protocols, or your customer support history. We fine-tune open-weight models on your proprietary data so they become domain specialists that outperform general-purpose APIs for your specific use cases — at a fraction of the inference cost.
What we fine-tune
Large Language Models
Llama, Mistral, Qwen, Gemma, Phi — we choose the right base model for your task, then fine-tune with LoRA, QLoRA, or full fine-tuning depending on budget and scale.
Vision & Multimodal
CLIP, LLaVA, Qwen-VL for document understanding, product tagging, defect detection, medical imaging, and OCR workflows grounded in your visual corpus.
Small Language Models
2B–8B parameter models optimized for edge deployment — laptops, kiosks, factory terminals. Lower latency, no cloud dependency, full data control.
Embedding & Retrieval
Custom embedding models tuned to your semantic space so your RAG systems retrieve the right document the first time, not the tenth.
Our training stack
We run production fine-tuning on multi-GPU infrastructure using PyTorch, HuggingFace Transformers, Unsloth, and Axolotl. For data preparation we use DSPy, LangChain, and custom ETL pipelines. Experiment tracking runs on Weights & Biases and model serving uses vLLM, TGI, or Ollama depending on your deployment target.
What you get
- A fine-tuned model checkpoint you own outright — no licensing, no per-token fees
- Full training data pipeline, reproducible end-to-end from raw sources to final weights
- Evaluation harness that proves your model beats GPT-4/Claude on your specific task
- Deployment-ready artifacts — quantized GGUF, ONNX, or native safetensors
- Documentation, inference scripts, and knowledge transfer to your team
Turnaround
Most production-grade fine-tunes ship in 48 hours to 2 weeks depending on dataset size and model class. We don't waste cycles — every training run is justified by an eval metric before we spin up a GPU.