# LLM Fine-Tuning Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/llm-fine-tuning
> Markdown URL: https://aitinkerers.org/technologies/llm-fine-tuning.md
> Technology record last updated: 2026-03-13T06:47:22Z
> Generated: 2026-10-25T00:57:29Z

LLM Fine-Tuning: The process of adapting a pre-trained foundation model (e.g., Llama, Mistral) to a specific, high-value task using techniques like LoRA to boost accuracy and reduce compute costs.

LLM Fine-Tuning is the essential post-training step: it shifts a generalist Large Language Model into a domain-specific specialist. We take a base model (like Llama 3.1 8B) and train it on a small, high-quality dataset, a process known as Supervised Fine-Tuning (SFT). Critical to efficiency are Parameter-Efficient Fine-Tuning (PEFT) methods: LoRA and QLoRA drastically reduce the trainable parameters, enabling customization of massive models (70B+) on less hardware. This results in superior, consistent performance for target applications, such as generating on-brand customer support responses or classifying legal documents with 99%+ accuracy.

- Official technology site: https://github.com/pytorch/torchtune
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AI call centre on WhatsApp](https://dhaka.aitinkerers.org/talks/rsvp_7WOKpHWjCKI)

- Event context: AI Tinkerers Dhaka Inaugural Meetup - Dhaka Builds with AI — 2025-11-01 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_7WOKpHWjCKI

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