TLILIFIRAS/Efficient-Fine-Tuning-of-Vision-Language-Models-with-LoRA-Quantization
This project demonstrates parameter-efficient fine-tuning of large Vision-Language Models (VLMs), specifically Qwen2-VL-7B-Instruct, using LoRA (Low-Rank Adaptation) and 4-bit quantization.
Stars
—
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 15, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/TLILIFIRAS/Efficient-Fine-Tuning-of-Vision-Language-Models-with-LoRA-Quantization"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
OptimalScale/LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
adithya-s-k/AI-Engineering.academy
Mastering Applied AI, One Concept at a Time
jax-ml/jax-llm-examples
Minimal yet performant LLM examples in pure JAX
young-geng/scalax
A simple library for scaling up JAX programs
riyanshibohra/TuneKit
Upload your data → Get a fine-tuned SLM. Free.