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The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek-VL: Towards Real-World Vision-Language Understanding
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
DeepSeek LLM: Let there be answers
A curated list of open-source projects related to DeepSeek Coder
DeepSeek Coder: Let the Code Write Itself
A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
[ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
Xwin-LM: Powerful, Stable, and Reproducible LLM Alignment
[ICCV2023] DETR Doesn’t Need Multi-Scale or Locality Design
OpenChat: Advancing Open-source Language Models with Imperfect Data
[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving
Emu Series: Generative Multimodal Models from BAAI
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
A large-scale 7B pretraining language model developed by BaiChuan-Inc.
A 13B large language model developed by Baichuan Intelligent Technology
This is an official implementation of our CVPR 2023 paper "Revealing the Dark Secrets of Masked Image Modeling" on Depth Estimation.
Painter & SegGPT Series: Vision Foundation Models from BAAI
[CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
EVA Series: Visual Representation Fantasies from BAAI
There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable an…
Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.