Translated100+ Chinese Word Vectors
- chinese
- chinese-word-segmentation
- embeddings
- word-embeddings
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Translated100+ Chinese Word Vectors
The official GitHub page for the survey paper "A Survey of Large Language Models".
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
A PyTorch-based Speech Toolkit
Low-code framework for building custom LLMs, neural networks, and other AI models
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
Build, Manage and Deploy AI/ML Systems
TranslatedPre-Training with Whole Word Masking for Chinese BERT (Chinese BERT-wwm series models)
Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own
Blender addons to make the bridge between Blender and geographic data
Run 35B MoE models in ~2.5 GB of RAM. Weights stream from SSD; a trained router predicts loads a step ahead. Mac, iPhone, Android, Windows supported
Infrastructure for continually self‑improving agents
TranslatedOpen-source manuscript of 《深入理解 AI Infra:量化分析与系统设计》 (In-Depth Understanding of AI Infra: Quantitative Analysis and System Design), by 李博杰 (Li Bojie): starting from hardware constraints and model architecture, it quantitatively derives the system design of LLM inference and training. Includes the full text of the book, a PDF, and companion calculation tools and experiments
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility