Custom LLM Training and Fine-Tuning
A general model knows a little about everything and a lot about nothing of yours. When prompts and retrieval hit a ceiling, fine-tuning teaches the model your format, your vocabulary and your judgement, and a smaller model can often do it for a fraction of the price.
In short. Custom LLM Training and Fine-Tuning is one of Matrida Infotech's AI services. Domain-specific language models fine-tuned or distilled on your data, measured against your tasks, and delivered on weights you own. Typical stack: PyTorch · Hugging Face · LoRA and QLoRA · vLLM · open-weight models · Weights & Biases.
Sound familiar?
- A large model gets the job done but costs too much per call
- Output format and tone keep drifting, whatever the prompt says
- Your data cannot leave your environment
- Prompting has stopped improving the score
Capabilities
- Fine-tuning and post-training
- Distillation into small models
- Preference and reward tuning
- Domain evaluation sets
- Model hosting and versioning
- Open-weight model selection
What we build
- A baseline first: prompted and retrieval-based versions scored on your tasks, so fine-tuning is only done where it pays
- Training data prepared from your own examples, with a held-out test set that is never trained on
- Fine-tuned or distilled models sized to the job, from small and cheap to large and strong
- Preference tuning where "good" is a matter of judgement
- Hosting, versioning and rollback, so a new model version is a controlled release
What you get
- Trained model weights and training recipe
- Evaluation report against the prompted baseline
- Serving setup and cost per call
- Retraining plan
Tools and frameworks
- PyTorch
- Hugging Face
- LoRA / QLoRA
- vLLM
- Axolotl
- Weights & Biases
Questions
When is fine-tuning better than RAG or prompting?
Retrieval is for facts that change; fine-tuning is for behaviour that should not: format, tone, domain reasoning, tool use. We test the cheaper options first and fine-tune only when the scores show a gap that matters.
How much data do we need?
Often a few hundred to a few thousand good examples for a focused task. Quality matters far more than volume. We check what you have in the first week and say honestly if it is not enough yet.
Who owns the trained model?
You do. The weights, training data preparation and recipe are handed over, and the model runs in your environment if you choose.
Will a small model really match a large one?
On a narrow, well-defined task it often can, and sometimes beats it. On broad open-ended work it usually cannot. The evaluation on your tasks decides, not a general benchmark.
Related services
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We build AI agents that complete multi-step work inside your systems, with guardrails, human approval and full audit logs.
Learn more →RAG & Enterprise Knowledge Assistants
Private assistants that answer from your documents with citations, permission-aware retrieval and measured accuracy.
Learn more →Generative AI Application Development
Customer-facing and internal LLM products, from document drafting to AI copilots inside your SaaS.
Learn more →Planning Custom LLMs? Tell us the workflow.
Say what is going wrong today. We reply with how custom llm training and fine-tuning would handle it, a rough scope and the first thing to build.
- You would get: trained model weights and training recipe
- You would get: evaluation report against the prompted baseline
- You would get: serving setup and cost per call