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This repository contains the code and data for the paper "SelfIE: Self-Interpretation of Large Language Model Embeddings" by Haozhe Chen, Carl Vondrick, and Chengzhi Mao.
A series of math-specific large language models of our Qwen2 series.
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
A simple, unified multimodal models training engine. Lean, flexible, and built for hacking at scale.
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
An interface library for RL post training with environments.
Enabling Efficient Reasoning in LLMs via Black-box Adversarial Prompting
starVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq]
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
本仓库包含对 Claude Code v1.0.33 进行逆向工程的完整研究和分析资料。包括对混淆源代码的深度技术分析、系统架构文档,以及重构 Claude Code agent 系统的实现蓝图。主要发现包括实时 Steering 机制、多 Agent 架构、智能上下文管理和工具执行管道。该项目为理解现代 AI agent 系统设计和实现提供技术参考。
PaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure
MineContext is your proactive context-aware AI partner(Context-Engineering+ChatGPT Pulse)
A command-line interface tool for serving LLM using vLLM.
A curated list of resources on Reinforcement Learning with Verifiable Rewards (RLVR) and the reasoning capability boundary of Large Language Models (LLMs).
A Unified Framework for High-Performance and Extensible LLM Steering
Fully Open Framework for Democratized Multimodal Training
Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
Context7 MCP Server -- Up-to-date code documentation for LLMs and AI code editors
Lightweight coding agent that runs in your terminal
Stanford NLP Python library for benchmarking the utility of LLM interpretability methods