I will fine tune and deploy custom llms for your specific use case

Fine-tuning is best for style, tone, format, and specialized tasks. RAG is better for factual recall over large knowledge bases. Often both work together. Message me with your use case and I'll tell you honestly which approach fits — I won't sell fine-tuning if you don't need it.

Comfortably up to 13B with QLoRA on a single A100 or 4090. Larger models (30B-70B) need multi-GPU setups — message me for a custom quote. I work with Llama 2/3, Mistral, Mixtral, Phi, Gemma, Qwen, and most open-source LLMs on Hugging Face.

You always get the full training code, configs, and a notebook you can re-run. The goal is for you to retrain or iterate without depending on me. I include detailed code comments and a README explaining how to load the adapter, run inference, and continue training.

No honest engineer can guarantee metrics before seeing your data. Quality depends on dataset quality, size, and task difficulty. Before accepting an order, I'll review a sample of your data and set realistic expectations. If it's not suitable, I'll tell you upfront.

Compute is billed separately. For small LoRA jobs on ≤7B models, I can use Colab Pro or Kaggle. For larger jobs, you provide cloud credits (AWS/GCP/RunPod) or I rent GPUs at cost with receipts. Typical compute: $20-200 depending on model size and dataset.

I work with JSONL, CSV, Parquet, Alpaca, ShareGPT, and most common structures. If your data is messy or in an unusual format, the Standard and Premium tiers include cleaning and reformatting. For Basic, please provide cleaned and properly formatted data.

A revision is one additional training run with adjusted hyperparameters or minor data changes (up to ~10% of dataset) based on your feedback. Revisions do NOT include changing the base model, redefining the task, or major dataset replacements — those need a new order.

Basic deployment (Ollama-compatible GGUF on a single machine) is available as an extra. For full production deployment with API endpoints, autoscaling, or cloud infrastructure, please check my dedicated deployment gig or message me for a custom quote.