mlbio-epfl/joint-inference
[ICLR 2025] Large (Vision) Language Models are Unsupervised In-Context Learners
This project helps machine learning practitioners improve the performance of existing large language models (LLMs) and vision-language models (VLMs) on specific tasks. You provide your chosen LLM or VLM along with a dataset, and the system uses an unsupervised adaptation framework to make the model perform better. This is useful for researchers and data scientists working with advanced AI models who want to enhance model accuracy without needing labeled ground truth data.
No commits in the last 6 months.
Use this if you need to boost the accuracy of your large language or vision-language models on tasks without access to costly labeled training data.
Not ideal if you are looking for a pre-trained model or a solution that does not require some technical expertise in machine learning model setup and execution.
Stars
22
Forks
6
Language
Python
License
MIT
Category
Last pushed
Jun 06, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mlbio-epfl/joint-inference"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
open-mmlab/mmpretrain
OpenMMLab Pre-training Toolbox and Benchmark
facebookresearch/mmf
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
adambielski/siamese-triplet
Siamese and triplet networks with online pair/triplet mining in PyTorch
HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis
Papers, code and datasets about deep learning and multi-modal learning for video analysis
KaiyangZhou/pytorch-vsumm-reinforce
Unsupervised video summarization with deep reinforcement learning (AAAI'18)