mlu-ai

mlu-ai's Stars

• chaoswork/llm_illustrated

  看图学大模型

  Language:Python20613

• astral-sh/uv

  An extremely fast Python package and project manager, written in Rust.

  Language:Rust28.1k806

• WangRongsheng/awesome-LLM-resourses

  🧑‍🚀 全世界最好的LLM资料总结 | Summary of the world's best LLM resources.

  2.5k296

• miguelgrinberg/retrofun

  Code from my "SQLAlchemy 2 In Practice" book.

  Language:Python9316

• HandsOnLLM/Hands-On-Large-Language-Models

  Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

  Language:Jupyter Notebook2.4k486

• david-cortes/contextualbandits

  Python implementations of contextual bandits algorithms

  Language:Python753148

• crewAIInc/crewAI

  Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

  Language:Python21.8k3k

• FidelusAleksander/ghcertified

  Prepare for GitHub Certification exams!

  Language:HTML433143

• openai/tiktoken

  tiktoken is a fast BPE tokeniser for use with OpenAI's models.

  Language:Python12.5k857

• google/sentencepiece

  Unsupervised text tokenizer for Neural Network-based text generation.

  Language:C++10.3k1.2k

• binhnguyennus/awesome-scalability

  The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

  59.2k6.1k

• bitsandbytes-foundation/bitsandbytes

  Accessible large language models via k-bit quantization for PyTorch.

  Language:Python6.3k637

• unslothai/unsloth

  Finetune Llama 3.2, Mistral, Phi, Qwen 2.5 & Gemma LLMs 2-5x faster with 80% less memory

  Language:Python18.7k1.3k

• PacktPublishing/Learn-React-with-TypeScript-2nd-Edition

  Learn React with TypeScript (2nd Edition), published by Packt

  Language:TypeScript14364

• PacktPublishing/React-and-React-Native-5E

  React and React Native, 5E_published by Packt

  Language:TypeScript4224

• amazon-science/auto-rag-eval

  Code repo for the ICML 2024 paper "Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation"

  Language:Python6711

• Denis2054/Transformers-for-NLP-and-Computer-Vision-3rd-Edition

  Transformers 3rd Edition

  Language:Jupyter Notebook339127

• CHIANGEL/Awesome-LLM-for-RecSys

  Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.

  1.1k58

• stanfordnlp/dspy

  DSPy: The framework for programming—not prompting—language models

  Language:Python19.4k1.5k

• bookingcom/upliftml

  UpliftML: A Python Package for Scalable Uplift Modeling

  Language:Python31828

• PacktPublishing/Synthetic-Data-for-Machine-Learning

  Synthetic Data for Machine Learning, published by Packt

  Language:Python94

• truera/trulens

  Evaluation and Tracking for LLM Experiments

  Language:Python2.2k192

• ddehueck/skip-gram-negative-sampling

  A PyTorch Implementation of the Skipgram Negative Sampling Word2Vec Model as Described in Mikolov et al.

  Language:Python153

• lukysummer/SkipGram_with_NegativeSampling_Pytorch

  Implementation of Word2Vec: Skip Grams with Negative Sampling method in Pytorch to generate context words from vocabulary given a single input word

  Language:Jupyter Notebook233

• PacktPublishing/Generative-AI-for-Cloud-Solutions

  Generative AI for Cloud Solutions, published by Packt

  Language:Jupyter Notebook57

• ehassler/MontgomeryDAE

  An R package for Design and Analysis of Experiment

  Language:R104

• Stonesjtu/Pytorch-NCE

  The Noise Contrastive Estimation for softmax output written in Pytorch

  Language:Python31745

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python56.7k5.8k

• ModelTC/lightllm

  LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.

  Language:Python2.6k210

• mli/paper-reading

  深度学习经典、新论文逐段精读

  27.3k2.5k