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Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
No fortress, purely open ground. OpenManus is Coming.
Witness the aha moment of VLM with less than $3.
Fully open reproduction of DeepSeek-R1
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
ImageBind One Embedding Space to Bind Them All
[CVPR 2024 Highlight🔥] Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Official code for Goldfish model for long video understanding and MiniGPT4-video for short video understanding
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
Code and models for ICML 2024 paper, NExT-GPT: Any-to-Any Multimodal Large Language Model
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts…
⚡️HivisionIDPhotos: a lightweight and efficient AI ID photos tools. 一个轻量级的AI证件照制作算法。
Monkey (LMM): Image Resolution and Text Label Are Important Things for Large Multi-modal Models (CVPR 2024 Highlight)
tc-mb / llama.cpp
Forked from ggml-org/llama.cppPort of Facebook's LLaMA model in C/C++
MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone
ReLE中文大模型能力评测（持续更新）：目前已囊括257个大模型，覆盖chatgpt、gpt-4.1、o4-mini、谷歌gemini-2.5、Claude、智谱GLM-Z1、文心一言、qwen-max、百川、讯飞星火、商汤senseChat、minimax等商用模型， 以及DeepSeek-R1-0528、qwq-32b、deepseek-v3、qwen3、llama4、phi-4、glm…
llama3 implementation one matrix multiplication at a time
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
The code of our paper "InfLLM: Unveiling the Intrinsic Capacity of LLMs for Understanding Extremely Long Sequences with Training-Free Memory"