Llama 3 or Mistral Fine-Tuning With LoRA on Your Custom Dataset

I will fine-tune a Llama 3 or Mistral open-source model on your custom dataset using Low-Rank Adaptation (LoRA) or QLoRA — enabling parameter-efficient fine-tuning on consumer or cloud GPU hardware — and deliver the trained adapter weights, merged model checkpoint, and inference code so you can run your custom model privately without ongoing API costs or data leaving your infrastructure. Open-source fine-tuning gives you full control over the model, your training data, and your deployment environment — critical for businesses with sensitive data, proprietary knowledge, or cost structures that make per-token API pricing unviable at scale.

The service covers environment setup (Google Colab, Vast.ai, RunPod, or your own GPU), base model loading with quantisation (4-bit or 8-bit via bitsandbytes), LoRA adapter configuration, supervised fine-tuning with the Hugging Face Trainer or TRL SFTTrainer, training metrics monitoring, adapter merging and model export, and a performance comparison between the base model and your fine-tuned version on held-out test examples.

Ideal for businesses and developers who want a privately hosted, domain-adapted language model without vendor lock-in or the ongoing inference costs of closed-source API providers.