This project (fine-tune.app) provides a full workflow for end-to-end fine-tuning of LLMs using user-uploaded data. The web UI and backend allow users to upload TXT, PDF, and DOCX files. The system automatically converts these files into high-quality JSONL training datasets, fine-tunes the Gemma 270M model, converts the result to GGUF, and deploys it to Ollama. The output model can then run fully locally, even on smaller devices and without an internet connection. This is particularly useful in domain-specific environments, privacy-sensitive settings, and cases where external APIs cannot be used.
More from Gemini Vibe Code Hackathon – London
SpriteFlow is an AI-driven asset workflow builder for automation in video game asset creation.

Cache Cow is a VS Code extension that solves the critical, post-generation problem of Vibe Debugging (Problem Statement Three). While "vibe coding" with AI is…
Codeminder is a hybrid LLM-based SAST (Static Application Security Testing) tool. It combines the speed and precision of traditional pattern-based scanning…

AI-Powered Case Study Platform for B2B Companies
This is a permanent “Museum of Vibe Coding”, built by Vibe Coding Collective, an international nonprofit. The goal is to ensure our community's creations no…
PatchGuard MCP is a lightweight server that automatically checks code generated by gemini for security issues, unsafe patterns, and lint problems. It creates…
