LLM Fine-Tuning Services
LLM Fine-Tuning Services — Train Models on Your Data
We fine-tune open-source and commercial language models on your proprietary data — delivering domain-specific accuracy, reduced hallucinations, and lower inference costs compared to generic API models.
Our Fine-Tuning Capabilities
Parameter-Efficient Fine-Tuning (PEFT)
Use LoRA and QLoRA to fine-tune large models on consumer or cloud GPUs — dramatically reducing compute costs while achieving performance comparable to full fine-tuning.
Domain Adaptation
Specialize models for your industry — legal, medical, finance, code, or customer support — so they understand your terminology, formats, and domain-specific reasoning patterns.
Instruction Tuning & RLHF
Shape model behavior through instruction fine-tuning and preference alignment — making your model follow instructions precisely and respond in the tone your users expect.
Evaluation & Benchmarking
Rigorous evaluation frameworks comparing base vs. fine-tuned model performance across accuracy, latency, cost per token, and domain-specific benchmarks.
What You Get
A production-ready fine-tuned model with full documentation, evaluation reports, and deployment infrastructure — not just model weights.
- Training dataset preparation, cleaning, and formatting
- Base model selection (Llama, Mistral, Phi, Gemma, or commercial)
- LoRA / QLoRA fine-tuning on your proprietary data
- Evaluation suite comparing base vs. fine-tuned performance
- Model deployment via API (vLLM, Ollama, cloud endpoints)
- Cost and latency analysis vs. GPT-4 / Claude alternatives
Tech Stack
Related Services
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From initial scoping to production deployment — we partner with you end-to-end. Let's start with a conversation.