JessicaGao8
Follow
Stars
这是一个从头训练大语言模型的项目，包括预训练、微调和直接偏好优化，模型拥有1B参数，支持中英文。
My learning notes/codes for ML SYS.
[NAACL'25] Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering
Official code implementation of General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
Daily updated LLM papers. 每日更新 LLM 相关的论文，欢迎订阅 👏 喜欢的话动动你的小手 🌟 一个
Awesome-Paper-list: Visualization meets LLM
DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
pdf-translator translates English PDF files into Japanese, preserving the original layout.
PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.
A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具，将PDF转换成Markdown和JSON格式。
WWW2025 Multimodal Intent Recognition for Dialogue Systems Challenge
MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
🧑🚀 全世界最好的LLM资料总结（数据处理、模型训练、模型部署、o1 模型、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.
Utilities intended for use with Llama models.
Code and Dataset for Learning to Solve Complex Tasks by Talking to Agents
An automatic prosodic boundary annotation tool for Text-to-Speech Synthesis (TTS).
WavJourney: Compositional Audio Creation with LLMs
[T-PAMI] A curated list of self-supervised multimodal learning resources.
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audi…
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
Yummly Similar Recipe And Similar Ingredient Recommender
🏦 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。