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Tsinghua University
- Beijing, People's Republic of China
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18:14
(UTC +08:00) - blog.huangfusl.net
- https://space.bilibili.com/387905326
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Everything you need to build state-of-the-art foundation models, end-to-end.
RAGEN is the first open-source reproduction of DeepSeek-R1 on AGENT training.
A curated list of resources on cold-start recommendations.
Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
为键盘工作者设计的单词记忆与英语肌肉记忆锻炼软件 / Words learning and English muscle memory training software designed for keyboard workers
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
坚持分享 GitHub 上高质量、有趣实用的开源技术教程、开发者工具、编程网站、技术资讯。A list cool, interesting projects of GitHub.
Yelp Simulator for WWW'25 AgentSociety Challenge
This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc in Pytorch, Tensorflow, Keras
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & RingAttention & RFT)
🔔🧠 Easily experiment with popular language agents across diverse reasoning/decision-making benchmarks!
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas…
A high-throughput and memory-efficient inference and serving engine for LLMs
A list of awesome papers and resources of recommender system on large language model (LLM).
Writing AI Conference Papers: A Handbook for Beginners
⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~
Reference implementation for DPO (Direct Preference Optimization)