Make Any LLM Work Better on Your Specific Task
LLM fine-tuning takes an existing language model and trains it further on your data so it performs better on your specific tasks. Instead of building a model from scratch, you start with a strong foundation (GPT-4, Llama 3, Mistral) and adapt it to your domain. The result is a model that understands your terminology, follows your formatting requirements, and produces outputs tailored to your use case.
Fine-tuning is often the most cost-effective path to a specialized AI system. It requires less data and compute than training from scratch, delivers results faster, and can dramatically improve performance on domain-specific tasks compared to prompt engineering alone.
Fine-Tuning Techniques We Use
- LoRA (Low-Rank Adaptation) - Trains a small set of adapter weights on top of the frozen base model. Fast, memory-efficient, and produces near-full-fine-tuning quality for most tasks.
- QLoRA - Quantized LoRA that allows fine-tuning large models on consumer-grade GPUs. Ideal for teams with limited compute budgets.
- RLHF (Reinforcement Learning from Human Feedback) - Aligns model outputs with human preferences. Used when you need the model to follow specific tone, safety, or quality guidelines.
- DPO (Direct Preference Optimization) - A simpler alternative to RLHF that achieves similar alignment results with less complexity. Works well with smaller datasets.
- Full fine-tuning - Updates all model weights. Used for large-scale domain adaptation where you have substantial training data and compute budget.
What Our Fine-Tuning Service Includes
- Task definition and benchmarking - We define exactly what the model needs to do, create evaluation benchmarks, and measure baseline performance before fine-tuning.
- Dataset creation - We build instruction-following datasets from your raw data, including prompt-completion pairs, preference data for RLHF/DPO, and validation sets.
- Training and optimization - We run training experiments with hyperparameter tuning, track metrics through Weights & Biases or MLflow, and select the best checkpoint.
- Evaluation report - You get a detailed report comparing fine-tuned performance against baseline on your specific benchmarks, with example outputs and error analysis.
- Deployment support - We help deploy the fine-tuned model to your infrastructure with inference optimization (quantization, batching, caching) for production use.
Fine-Tuning vs. RAG vs. Prompting
Prompt engineering works for simple customization. RAG works when the model needs access to current or private data at query time. Fine-tuning works when you need the model to behave differently at a fundamental level: following specific formats, using domain terminology correctly, or consistently producing a particular style of output. We help you choose the right approach for each use case.
Start Fine-Tuning
Book a free consultation. We will assess your use case, review your available data, and recommend the right fine-tuning technique and base model for your needs.