Turn open-source language models into domain-specific powerhouses. From data preparation to deployment — I fine-tune LLMs that understand your business.
End-to-end LLM customization — from data curation to optimized inference
High-quality training data from your documents, conversations, and domain knowledge. Data cleaning, deduplication, and instruction-format conversion.
Parameter-efficient fine-tuning that delivers 95%+ of full fine-tuning quality at a fraction of the compute cost. Ideal for domain adaptation on a budget.
Train models to follow complex instructions, maintain persona, and produce structured outputs. Multi-turn conversation fine-tuning for chatbot applications.
Align models with human preferences using Reinforcement Learning from Human Feedback or Direct Preference Optimization for safer, higher-quality outputs.
Quantization (GPTQ, AWQ, GGUF), distillation, and pruning to reduce model size and latency while maintaining quality. Serve models at 2–4x lower cost.
Deploy fine-tuned models with vLLM, TGI, or Triton. Auto-scaling inference endpoints on GPU clusters, with monitoring and A/B testing.
Deep experience across the leading open-source LLM ecosystem
Latest Llama and Code Llama families. Best general-purpose open model family.
8B / 70B / 405BMistral 7B, Mixtral 8x7B MoE. Excellent quality-to-size ratio.
7B / 8x7B MoEDeepSeek V2, DeepSeek Coder. Leading models for code and reasoning.
Coder / ChatQwen 2.5, Qwen Chat. Strong multilingual and coding capabilities.
7B / 72BPhi-3, Phi-3.5. Exceptional small models for edge and mobile deployment.
3.8B / 14BGemma 2, CodeGemma. Lightweight models with excellent instruction following.
2B / 9B / 27BTransparent pricing for every fine-tuning project size
Single model, small dataset
1–2 week delivery
Full pipeline + deployment
3–5 week delivery
Multi-model platform
6–10 week delivery
Fine-tuning is best when you need the model to learn a specific style, format, or domain knowledge that's hard to express in prompts. RAG is better for dynamic, factual knowledge retrieval. Prompt engineering works for straightforward tasks. Often, the best solution combines all three.
Quality matters more than quantity. For LoRA fine-tuning, 1,000–5,000 high-quality examples often produce excellent results. For full fine-tuning, 10,000–50,000+ examples are ideal. I can help create synthetic training data to supplement smaller datasets.
With quantization (4-bit AWQ/GPTQ), a 7B model runs on a single T4 GPU, and a 70B model on 2x A100s. I optimize models to minimize hardware requirements. Cloud deployment on AWS, GCP, or serverless GPU providers (Modal, RunPod) is also an option.
Absolutely. All fine-tuned model weights and training data remain your intellectual property. Models are deployed on your infrastructure or private cloud accounts. I sign NDAs and follow strict data handling protocols.
I use a combination of automated metrics (perplexity, BLEU, ROUGE, exact match) and human evaluation. For instruction-tuned models, I create domain-specific eval sets and run blind A/B comparisons against the base model and competing solutions.
Let's discuss your use case and find the perfect model and fine-tuning approach for your needs.