Use PEFT or Full-parameter to finetune 300+ LLMs or 80+ MLLMs. (Qwen2, GLM4v, Internlm2.5, Yi, Llama3.1, Llava-Video, Internvl2, MiniCPM-V-2.6, Deepseek, Baichuan2, Gemma2, Phi3-Vision, ...)

Utilities intended for use with Llama models.

MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone

Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud.

GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型

Retrieval and Retrieval-augmented LLMs

[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型

GPT4V-level open-source multi-modal model based on Llama3-8B

The official repo of Qwen2-Audio chat & pretrained large audio language model proposed by Alibaba Cloud.

The official repo of Qwen-VL (通义千问-VL) chat & pretrained large vision language model proposed by Alibaba Cloud.

A series of large language models developed by Baichuan Intelligent Technology

Face recognition using Tensorflow

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Graph Convolutional Networks for Text Classification. AAAI 2019

深度学习入门教程&&优秀文章&&Deep Learning Tutorial

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

An industrial deep learning framework for high-dimension sparse data

Models and examples built with TensorFlow

Code for the paper "Language Models are Unsupervised Multitask Learners"

A library for efficient similarity search and clustering of dense vectors.

2019届秋招面经集合

A distributed graph deep learning framework.

通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP