Custom models
LLM Training & Fine-Tuning
When prompting and RAG hit their limits, a fine-tuned model can be cheaper, faster, and more on-brand. I handle the data, the training, and the evaluation - end to end.
What's included
Custom models, with the data behind them
Scope
- Fine-tuning open models (LLaMA, Mistral) with LoRA / QLoRA
- Instruction & preference dataset curation
- Evaluation, guardrails & regression testing
- Serving, quantization & cost optimization
How it works
Engagement
- Decide: prompt vs RAG vs fine-tune (honest cost/benefit)
- Dataset construction & cleaning
- Train, evaluate, iterate against a baseline
- Deploy & hand over reproducible pipelines
Proof
- Production ML across PyTorch, scikit-learn, XGBoost
- Speech-to-text & NLP pipelines at 99% accuracy
- Model training & team enablement for non-technical stakeholders
FAQ
Common Questions
Is fine-tuning worth it versus just using GPT or Claude?
Often not at first - prompting plus RAG covers most needs. Fine-tuning pays off when you need consistent style/format, lower per-call cost at scale, on-prem/private deployment, or a narrow task a general model does inconsistently. I will tell you honestly when it is not worth it.
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