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Johns Hopkins University
- Maryland, USA
- https://vibashan.github.io/
Stars
Models and examples built with TensorFlow
A high-throughput and memory-efficient inference and serving engine for LLMs
A natural language interface for computers
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Making large AI models cheaper, faster and more accessible
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
OpenMMLab Detection Toolbox and Benchmark
Code and documentation to train Stanford's Alpaca models, and generate the data.
Python sample codes and textbook for robotics algorithms.
Generative Models by Stability AI
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Fully open reproduction of DeepSeek-R1
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
verl: Volcano Engine Reinforcement Learning for LLMs
[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Bringing Old Photo Back to Life (CVPR 2020 oral)
Wan: Open and Advanced Large-Scale Video Generative Models
Ongoing research training transformer models at scale
Wan: Open and Advanced Large-Scale Video Generative Models
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3, Qwen3-MoE, DeepSeek-R1, GLM4.5, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Llava, …
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.




