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Fine-Tuning

Make LLMs Speak Your Language

Prompt engineering hits a ceiling. Fine-tuning breaks through it. We adapt foundation models to your domain — better accuracy, lower costs, faster inference, and outputs that sound like your experts wrote them.

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Models We Fine-Tune

OpenAI GPT-4 / GPT-4o
Anthropic Claude
Meta Llama 3
Mistral / Mixtral
Google Gemma
Microsoft Phi
Cohere Command
Any Hugging Face model

Fine-Tuning Capabilities

Supervised Fine-Tuning

Train models on your labeled examples — customer support transcripts, legal documents, medical notes, or any domain-specific data.

LoRA & QLoRA

Efficient fine-tuning that adapts foundation models with minimal compute. Run fine-tuned Llama or Mistral on a single GPU.

RLHF & DPO

Reinforcement Learning from Human Feedback and Direct Preference Optimization. Align model outputs with your quality standards.

Data Preparation

Fine-tuning is only as good as the training data. We clean, format, deduplicate, and validate your dataset before training.

Evaluation & Benchmarking

Domain-specific eval suites, A/B testing against base models, and production metric tracking. Prove the fine-tune actually works.

Deployment & Serving

Deploy fine-tuned models on your infrastructure — AWS, Azure, GCP, or on-prem. Optimized serving with vLLM, TGI, or Ollama.

Discuss Fine-Tuning

Tell us what your model needs to do better. We'll assess your data, recommend an approach, and deliver a fine-tuned model that outperforms the base.

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