LLM Fine-Tuning

AI & LLM

Fine-tuning open-source LLMs using LoRA and QLoRA with evaluation pipelines, adapting models to domain vocabulary, formats and tasks when prompting alone stops improving results.

We delivered

Fine-tuned model weights or adapters with license review, a curated training dataset and preparation scripts, an evaluation report comparing base and fine-tuned performance, and a deployment package for the client's serving stack.

Dataset curation, cleaning and instruction formatting. LoRA/QLoRA training on open-weight models (Llama, Mistral, Qwen). Evaluation pipelines comparing base, prompted and fine-tuned variants. Quantization and deployment packaging for inference.

Used for domain-specific assistants where prompting hits a quality ceiling, consistent structured output at scale, and cost reduction by replacing a large hosted model with a tuned smaller one.

Built with LoRA/QLoRA, PEFT, Axolotl, vLLM, Hugging Face and Weights & Biases/MLflow.