Open-Source LLM Fine-Tuning — Llama, Mistral & Qwen Models

I will fine-tune an open-weight LLM (Llama 3, Mistral, Qwen, or Phi) on your dataset using Axolotl or Unsloth — covering dataset preparation, LoRA or full fine-tuning configuration, training job execution on your GPU infrastructure, evaluation against held-out data, and delivery as a merged model ready for inference. Open-source LLM fine-tuning produces a model you own and can deploy privately — eliminating per-token API costs at scale, keeping your training data off third-party infrastructure, and giving you complete control over model updates and versioning that API-based fine-tuning does not provide.

The fine-tuning covers base model selection, dataset preparation, training configuration (LoRA rank, learning rate, epochs), GPU training execution, training curve monitoring, held-out evaluation, model merging, and delivery as a deployable model checkpoint.