Stars
DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling
The official repo for the paper: SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans.
Train your Agent model via our easy and efficient framework
DSPy: The framework for programming—not prompting—language models
AgentSearch is a framework for powering search agents and enabling customizable local search.
🔍 Search-o1: Agentic Search-Enhanced Large Reasoning Models [EMNLP 2025]
DiSCo for Conversational Information Seeking with SPLADE [SIGIR 2025]
⌥ AI Coding agent for the terminal — hash-anchored edits, optimized tool harness, LSP, Python, browser, subagents, and more
⚡ Super fast clustering for high-dimensional vectors on CPUs (x86, ARM) and GPUs — for Python and C++. 100x faster clustering of vector embeddings than FAISS
Tools for merging pretrained large language models.
SGLang is a high-performance serving framework for large language models and multimodal models.
My learning notes for ML SYS.
A version of verl to support diverse tool use [TMLR 2026]
Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
AgentCPM-GUI: An on-device GUI agent for operating Android apps, enhancing reasoning ability with reinforcement fine-tuning for efficient task execution.
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!
An extensive and commented list of resources on Learned Sparse Retrieval.
This repo contains the Hugging Face Deep Reinforcement Learning Course.
Official repository for the SIGIR 2026 paper "Revisiting Text Ranking in Deep Research"
AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
[SIGIR 2025] The official repo for "Scaling Sparse and Dense Retrieval in Decoder-Only LLMs"
Distributed Compiler based on Triton for Parallel Systems