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A minimal codebase for finetuning large multimodal models, supporting llava-1.5/1.6, llava-interleave, llava-next-video, llava-onevision, llama-3.2-vision, qwen-vl, qwen2-vl, phi3-v etc.
Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought and OpenAI o1 🍓
Code for STaR: Bootstrapping Reasoning With Reasoning (NeurIPS 2022)
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, B…
Training Sparse Autoencoders on Language Models
Video+code lecture on building nanoGPT from scratch
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
Code and documentation to train Stanford's Alpaca models, and generate the data.
👨💻 An awesome and curated list of best code-LLM for research.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.
✨✨Latest Advances on Multimodal Large Language Models
Implementation of the InterpretTime framework
A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).
A Survey of Learning from Graphs with Heterophily
[AAAI'22] Event-Aware Multimodal Mobility Nowcasting
关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记
An elegant \LaTeX\ résumé template. 大陆镜像 https://gods.coding.net/p/resume/git
The datasets can be used for POI/next-POI recommendation, trajectory recommendation, friends recommendation (link prediction), activity recommendations, group recommendation and community discovery…
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"