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Excelius-Wang/README.md

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Excelius

LLM post-training · alignment · agents 🤖 — MSc @ BJTU

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About Me

  • 🎓 MSc in Software Engineering @ Beijing Jiaotong University; BEng @ Nantong University.
  • 🔬 I work on LLM post-training (SFT / LoRA / DPO), alignment & preference optimization (PPO / GRPO / reward modeling), LLM agents (LangChain / LangGraph), and efficient inference (vLLM).
  • 📝 Co-authored a research paper.
  • ✍️ I write notes & blog posts at excelius.xyz.

Experience

  • ByteDance — Multimodal LLM Algorithm Intern · present
  • Baidu — LLM Post-training Algorithm Intern
  • Tsinghua University, Institute of Vehicle Power & Intelligent Energy — LLM Application & Full-stack Intern

Tech Stack

  • Languages: Python, C++, TypeScript / JavaScript
  • LLM / Post-training: PyTorch, HuggingFace Transformers, LLaMA-Factory, ms-swift, vLLM
  • Alignment / RL: SFT, LoRA, DPO, PPO, GRPO, Reward Modeling (GenRM, LLM-as-a-Judge)
  • Agents: LangChain, LangGraph (ReAct, Tool Calling, Plan-and-Execute)
  • Infra: multi-node multi-GPU training, Git, Docker

Featured Projects

  • dive-into-transformer-pytorch ⭐6 — A Transformer language model built from scratch in PyTorch; trains an autoregressive classical-Chinese text generator on Dream of the Red Chamber, with multi-GPU data-parallel training, checkpointing, and loss visualization.
  • BERT_BiLSTM_CRF ⭐5 — Chinese named-entity recognition on a literary-prose corpus, built on BERT + BiLSTM + CRF (BJTU NLP coursework).
  • Self-DeepResearch — An autonomous deep-research agent on LangGraph + Tavily: plan → search → reflect → generate, iterating to a cited report; Vue frontend with a streaming backend.
  • marginalia — A deep paper-reading skill for Claude Code / Codex that reconstructs the author's reasoning, stress-tests its weakest assumptions, and publishes structured notes to a Feishu knowledge base.
  • PyTorchClassics — Classic deep-learning models re-implemented from scratch in PyTorch (MLP, LeNet, attention …) — an ongoing study collection.

📊 Dashboard

ghfind GitHub 评分卡

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    Building AI agents, atomically

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