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Vector (and Scalar) Quantization, in Pytorch
Scripts for processing the Amazon Reviews 2023 dataset; implementations and checkpoints of BLaIR: "Bridging Language and Items for Retrieval and Recommendation".
Replication of the paper "Text Is All You Need: Learning Language Representations for Sequential Recommendation" on KDD'23.
A simple but complete full-attention transformer with a set of promising experimental features from various papers
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
GPT4All: Chat with Local LLMs on Any Device
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
Hackable and optimized Transformers building blocks, supporting a composable construction.
Instruct-tune LLaMA on consumer hardware
Awesome-LLM: a curated list of Large Language Model
A modular RL library to fine-tune language models to human preferences
Making large AI models cheaper, faster and more accessible
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
Introducing Bundle Recommendation in Conversational Recommendation Scenarios on RecSys 2022
Codebase for SIGIR 2022 paper: Coarse-to-Fine Sparse Sequential Recommendation
Locally Constrained Self-Attentive Sequential Recommendation
An easy-to-use tool for phrase encoding and topic mining (unsupervised aspect extraction); Code base for ACL 2022 paper, UCTopic: Unsupervised Contrastive Learning for Phrase Representations and To…
2018/2019/校招/春招/秋招/自然语言处理(NLP)/深度学习(Deep Learning)/机器学习(Machine Learning)/C/C++/Python/面试笔记，此外，还包括创建者看到的所有机器学习/深度学习面经中的问题。 除了其中 DL/ML 相关的，其他与算法岗相关的计算机知识也会记录。 但是不会包括如前端/测试/JAVA/Android等岗位中有关的问题。
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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
A tool for extracting plain text from Wikipedia dumps
A repo for data science related questions and answers
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.