FineTune Studio exists because every fine-tuning tutorial I found assumed a rented A100 and a notebook full of half-explained flags. I wanted something a student could run: upload a dataset, validate it, launch a real QLoRA job, watch the loss stream live, then see — not assume — whether the fine-tuned model actually improved.
The numbers that matter
- 17 backend endpoints, 107 / 107 tests passing.
- Base model Qwen3-1.7B, peak VRAM ~3.2 GB.
- 3 inference paths: local, vLLM, or a Hugging Face Space — pick per deployment.
- 7 frontend pages: Dashboard, Datasets, Training, Live Logs, Evaluation, Experiments, Settings.
Dataset validation before a single GPU cycle
Most fine-tuning failures I've seen aren't hyperparameter problems, they're dataset problems — malformed JSON, duplicate examples, a prompt template mismatch. FineTune Studio validates the dataset on upload and reports the actual issue, not a stack trace three steps into training.
Live telemetry, not a log file to tail
Training streams loss, learning rate and throughput live over the same connection the frontend already holds — so a run's progress is a graph, not a terminal you keep alt-tabbing to.
The comparison is the whole point
If you can't show the before and after, you didn't fine-tune — you spent GPU hours. The Evaluation page runs the same prompts against the base model and the tuned model side by side. That comparison, more than the loss curve, is what tells you whether the run was worth it.
What I'd tell a student starting this
Don't buy a GPU first. Learn on the free tier and on small models — the memory limit teaches you more about your dependencies than any tutorial does. A 3B model with your own dataset and an honest eval beats a huge model behind an API you only rent for a demo.
Code and the full architecture: github.com/Pranjulrathour/FINETUNESTUDIO.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
