Stars
An extremely fast Python package and project manager, written in Rust.
DeepSeek-VL: Towards Real-World Vision-Language Understanding
华为视角看世界，任总思维看问题，华为任正非思想之路，Huawei Renzhengfei speech,email& article,整理自1994年开始的讲话稿，涉及财经、人力资源、战略、内控与公共关系，从交换机、通讯设备、移动终端到人工智能、物联网，从2G、3G到4G、5G，从物理学、化学、数学到心理学、哲学，是创业，学习的标杆素材。
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning …
Pass the Google Analytics Individual Qualification certification exam using Mind Map.
Awesome-RAG: Collect typical RAG papers and systems.
Analyze computation-communication overlap in V3/R1.
Curated tutorials and resources for Large Language Models, AI Painting, and more.
lianjia / beike estate crawler/analysis 2024
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Static memory-efficient Trie-like structures for Python based on marisa-trie C++ library.
Awesome implementations of the Mapbox Vector Tile specification
100+ Chinese Word Vectors 上百种预训练中文词向量
The hanzi similar tool.(汉字相似度计算工具，中文形近字算法。可用于手写汉字识别纠正，文本混淆等。)
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
The project is for PDF Python learning with Large Language Model.
This repo includes ChatGPT prompt curation to use ChatGPT and other LLM tools better.
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
MTEB: Massive Text Embedding Benchmark
text2vec, text to vector. 文本向量表征工具，把文本转化为向量矩阵，实现了Word2Vec、RankBM25、Sentence-BERT、CoSENT等文本表征、文本相似度计算模型，开箱即用。
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 500+ LLMs (Qwen3, Qwen3-MoE, Llama4, GLM4.5, InternLM3, DeepSeek-R1, ...) and 200+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, Llava, GLM4v, Ph…
Fast and accurate AI powered file content types detection
Official Code for Stable Cascade
Large World Model -- Modeling Text and Video with Millions Context