# finetuned models Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/finetuned-models
> Markdown URL: https://aitinkerers.org/technologies/finetuned-models.md
> Technology record last updated: 2026-05-05T12:22:42Z
> Generated: 2026-10-24T01:46:00Z

Fine-tuned models: Specialists created by adapting a broad pre-trained foundation model (e.g., Llama 3, GPT-4) on a smaller, high-quality, domain-specific dataset.

Fine-tuning is the critical process of taking a general-purpose pre-trained model and further training it on a task-specific dataset: this is transfer learning in action. The goal is specialization: adapting the model's weights to a specific domain (e.g., legal, medical, finance) to improve accuracy and relevance, not to inject new general knowledge. Techniques like Parameter-Efficient Fine-Tuning (PEFT), specifically LoRA, allow for this adaptation with minimal computational overhead (e.g., using a single GPU) and prevent catastrophic forgetting of the base model's capabilities. A fine-tuned, smaller model (e.g., Llama 3 8B) can often outperform a much larger, general model on its targeted task, reducing inference latency and cost in production environments.

- Official technology site: https://huggingface.co/docs/peft/en/index
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Internal tools are the new IP](https://toronto.aitinkerers.org/talks/rsvp_rn_wEKad7fI)

- Event context: AI Tinkerers Toronto - November 2024 Meetup at Accenture — 2024-11-28 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_rn_wEKad7fI

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