awesome-LLM-resourses

awesome-LLM-resourses

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

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A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

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全世界最好的大语言模型资源汇总 持续更新

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Contents

数据 Data

[!NOTE]

此处命名为数据，但这里并没有提供具体数据集，而是提供了处理获取大规模数据的方法

我们始终秉持授人以鱼不如授人以渔

  1. AotoLabel: Label, clean and enrich text datasets with LLMs.
  2. LabelLLM: The Open-Source Data Annotation Platform.
  3. data-juicer: A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs!
  4. OmniParser: a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.
  5. MinerU: MinerU is a one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.
  6. PDF-Extract-Kit: A Comprehensive Toolkit for High-Quality PDF Content Extraction.
  7. Parsera: Lightweight library for scraping web-sites with LLMs.
  8. Sparrow: Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images.
  9. Docling: Transform PDF to JSON or Markdown with ease and speed.
  10. GOT-OCR2.0: OCR Model.
  11. LLM Decontaminator: Rethinking Benchmark and Contamination for Language Models with Rephrased Samples.
  12. DataTrove: DataTrove is a library to process, filter and deduplicate text data at a very large scale.
  13. llm-swarm: Generate large synthetic datasets like Cosmopedia.
  14. Distilabel: Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
  15. Common-Crawl-Pipeline-Creator: The Common Crawl Pipeline Creator.
  16. Tabled: Detect and extract tables to markdown and csv.
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微调 Fine-Tuning

  1. LLaMA-Factory: Unify Efficient Fine-Tuning of 100+ LLMs.
  2. unsloth: 2-5X faster 80% less memory LLM finetuning.
  3. TRL: Transformer Reinforcement Learning.
  4. Firefly: Firefly: 大模型训练工具，支持训练数十种大模型
  5. Xtuner: An efficient, flexible and full-featured toolkit for fine-tuning large models.
  6. torchtune: A Native-PyTorch Library for LLM Fine-tuning.
  7. Swift: Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs.
  8. AutoTrain: A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models.
  9. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO).
  10. Ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models.
  11. mistral-finetune: A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models.
  12. aikit: Fine-tune, build, and deploy open-source LLMs easily!
  13. H2O-LLMStudio: H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs.
  14. LitGPT: Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.
  15. LLMBox: A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.
  16. PaddleNLP: Easy-to-use and powerful NLP and LLM library.
  17. workbench-llamafactory: This is an NVIDIA AI Workbench example project that demonstrates an end-to-end model development workflow using Llamafactory.
  18. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral).
  19. TinyLLaVA Factory: A Framework of Small-scale Large Multimodal Models.
  20. LLM-Foundry: LLM training code for Databricks foundation models.
  21. lmms-finetune: A unified codebase for finetuning (full, lora) large multimodal models, supporting llava-1.5, qwen-vl, llava-interleave, llava-next-video, phi3-v etc.
  22. Simplifine: Simplifine lets you invoke LLM finetuning with just one line of code using any Hugging Face dataset or model.
  23. Transformer Lab: Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
  24. Liger-Kernel: Efficient Triton Kernels for LLM Training.
  25. ChatLearn: A flexible and efficient training framework for large-scale alignment.
  26. nanotron: Minimalistic large language model 3D-parallelism training.
  27. Proxy Tuning: Tuning Language Models by Proxy.
  28. Effective LLM Alignment: Effective LLM Alignment Toolkit.
  29. Autotrain-advanced
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推理 Inference

  1. ollama: Get up and running with Llama 3, Mistral, Gemma, and other large language models.
  2. Open WebUI: User-friendly WebUI for LLMs (Formerly Ollama WebUI).
  3. Text Generation WebUI: A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
  4. Xinference: A powerful and versatile library designed to serve language, speech recognition, and multimodal models.
  5. LangChain: Build context-aware reasoning applications.
  6. LlamaIndex: A data framework for your LLM applications.
  7. lobe-chat: an open-source, modern-design LLMs/AI chat framework. Supports Multi AI Providers, Multi-Modals (Vision/TTS) and plugin system.
  8. TensorRT-LLM: TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
  9. vllm: A high-throughput and memory-efficient inference and serving engine for LLMs.
  10. LlamaChat: Chat with your favourite LLaMA models in a native macOS app.
  11. NVIDIA ChatRTX: ChatRTX is a demo app that lets you personalize a GPT large language model (LLM) connected to your own content—docs, notes, or other data.
  12. LM Studio: Discover, download, and run local LLMs.
  13. chat-with-mlx: Chat with your data natively on Apple Silicon using MLX Framework.
  14. LLM Pricing: Quickly Find the Perfect Large Language Models (LLM) API for Your Budget! Use Our Free Tool for Instant Access to the Latest Prices from Top Providers.
  15. Open Interpreter: A natural language interface for computers.
  16. Chat-ollama: An open source chatbot based on LLMs. It supports a wide range of language models, and knowledge base management.
  17. chat-ui: Open source codebase powering the HuggingChat app.
  18. MemGPT: Create LLM agents with long-term memory and custom tools.
  19. koboldcpp: A simple one-file way to run various GGML and GGUF models with KoboldAI's UI.
  20. LLMFarm: llama and other large language models on iOS and MacOS offline using GGML library.
  21. enchanted: Enchanted is iOS and macOS app for chatting with private self hosted language models such as Llama2, Mistral or Vicuna using Ollama.
  22. Flowise: Drag & drop UI to build your customized LLM flow.
  23. Jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer. Multiple engine support (llama.cpp, TensorRT-LLM).
  24. LMDeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
  25. RouteLLM: A framework for serving and evaluating LLM routers - save LLM costs without compromising quality!
  26. MInference: About To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
  27. Mem0: The memory layer for Personalized AI.
  28. SGLang: SGLang is yet another fast serving framework for large language models and vision language models.
  29. AirLLM: AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning. And you can run 405B Llama3.1 on 8GB vram now.
  30. LLMHub: LLMHub is a lightweight management platform designed to streamline the operation and interaction with various language models (LLMs).
  31. YuanChat
  32. LiteLLM: Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
  33. GuideLLM: GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs).
  34. LLM-Engines: A unified inference engine for large language models (LLMs) including open-source models (VLLM, SGLang, Together) and commercial models (OpenAI, Mistral, Claude).
  35. OARC: ollama_agent_roll_cage (OARC) is a local python agent fusing ollama llm's with Coqui-TTS speech models, Keras classifiers, Llava vision, Whisper recognition, and more to create a unified chatbot agent for local, custom automation.
  36. g1: Using Llama-3.1 70b on Groq to create o1-like reasoning chains.
  37. MemoryScope: MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
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评估 Evaluation

  1. lm-evaluation-harness: A framework for few-shot evaluation of language models.
  2. opencompass: OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
  3. llm-comparator: LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed.
  4. EvalScope
  5. Weave: A lightweight toolkit for tracking and evaluating LLM applications.
  6. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures.
  7. Evaluation guidebook: If you've ever wondered how to make sure an LLM performs well on your specific task, this guide is for you!
  8. Ollama Benchmark: LLM Benchmark for Throughput via Ollama (Local LLMs).
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体验 Usage

  1. LMSYS Chatbot Arena: Benchmarking LLMs in the Wild
  2. CompassArena 司南大模型竞技场
  3. 琅琊榜
  4. Huggingface Spaces
  5. WiseModel Spaces
  6. Poe
  7. 林哥的大模型野榜
  8. OpenRouter
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RAG

  1. AnythingLLM: The all-in-one AI app for any LLM with full RAG and AI Agent capabilites.
  2. MaxKB: 基于 LLM 大语言模型的知识库问答系统。开箱即用，支持快速嵌入到第三方业务系统
  3. RAGFlow: An open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
  4. Dify: An open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
  5. FastGPT: A knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization.
  6. Langchain-Chatchat: 基于 Langchain 与 ChatGLM 等不同大语言模型的本地知识库问答
  7. QAnything: Question and Answer based on Anything.
  8. Quivr: A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
  9. RAG-GPT: RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.
  10. Verba: Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.
  11. FlashRAG: A Python Toolkit for Efficient RAG Research.
  12. GraphRAG: A modular graph-based Retrieval-Augmented Generation (RAG) system.
  13. LightRAG: LightRAG helps developers with both building and optimizing Retriever-Agent-Generator pipelines.
  14. GraphRAG-Ollama-UI: GraphRAG using Ollama with Gradio UI and Extra Features.
  15. nano-GraphRAG: A simple, easy-to-hack GraphRAG implementation.
  16. RAG Techniques: This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
  17. ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.
  18. kotaemon: An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
  19. RAGapp: The easiest way to use Agentic RAG in any enterprise.
  20. TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text.
  21. LightRAG: Simple and Fast Retrieval-Augmented Generation.
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Agents

  1. AutoGen: AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen AIStudio
  2. CrewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
  3. Coze
  4. AgentGPT: Assemble, configure, and deploy autonomous AI Agents in your browser.
  5. XAgent: An Autonomous LLM Agent for Complex Task Solving.
  6. MobileAgent: The Powerful Mobile Device Operation Assistant Family.
  7. Lagent: A lightweight framework for building LLM-based agents.
  8. Qwen-Agent: Agent framework and applications built upon Qwen2, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.
  9. LinkAI: 一站式 AI 智能体搭建平台
  10. Baidu APPBuilder
  11. agentUniverse: agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications. Furthermore, through the community, they can exchange and share practices of patterns across different domains.
  12. LazyLLM: 低代码构建多Agent大模型应用的开发工具
  13. AgentScope: Start building LLM-empowered multi-agent applications in an easier way.
  14. MoA: Mixture of Agents (MoA) is a novel approach that leverages the collective strengths of multiple LLMs to enhance performance, achieving state-of-the-art results.
  15. Agently: AI Agent Application Development Framework.
  16. OmAgent: A multimodal agent framework for solving complex tasks.
  17. Tribe: No code tool to rapidly build and coordinate multi-agent teams.
  18. CAMEL: Finding the Scaling Law of Agents. A multi-agent framework.
  19. PraisonAI: PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.
  20. IoA: An open-source framework for collaborative AI agents, enabling diverse, distributed agents to team up and tackle complex tasks through internet-like connectivity.
  21. llama-agentic-system : Agentic components of the Llama Stack APIs.
  22. Agent Zero: Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it.
  23. Agents: An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents.
  24. AgentScope: Start building LLM-empowered multi-agent applications in an easier way.
  25. FastAgency: The fastest way to bring multi-agent workflows to production.
  26. Swarm: Framework for building, orchestrating and deploying multi-agent systems. Managed by OpenAI Solutions team. Experimental framework.
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搜索 Search

  1. OpenSearch GPT: SearchGPT / Perplexity clone, but personalised for you.
  2. MindSearch: An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT).
  3. nanoPerplexityAI: The simplest open-source implementation of perplexity.ai.
  4. curiosity: Try to build a Perplexity-like user experience.
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书籍 Book

  1. 《大规模语言模型：从理论到实践》
  2. 《大语言模型》
  3. 《动手学大模型Dive into LLMs》
  4. 《动手做AI Agent》
  5. 《Build a Large Language Model (From Scratch)》
  6. 《多模态大模型》
  7. 《Generative AI Handbook: A Roadmap for Learning Resources》
  8. 《Understanding Deep Learning》
  9. 《Illustrated book to learn about Transformers & LLMs》
  10. 《Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG》
  11. 《大型语言模型实战指南：应用实践与场景落地》
  12. 《Hands-On Large Language Models》
  13. 《自然语言处理：大模型理论与实践》
  14. 《动手学强化学习》
  15. 《面向开发者的LLM入门教程》
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课程 Course

  1. 斯坦福 CS224N: Natural Language Processing with Deep Learning
  2. 吴恩达: Generative AI for Everyone
  3. 吴恩达: LLM series of courses
  4. ACL 2023 Tutorial: Retrieval-based Language Models and Applications
  5. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
  6. 微软: Generative AI for Beginners
  7. 微软: State of GPT
  8. HuggingFace NLP Course
  9. 清华 NLP 刘知远团队大模型公开课
  10. 斯坦福 CS25: Transformers United V4
  11. 斯坦福 CS324: Large Language Models
  12. 普林斯顿 COS 597G (Fall 2022): Understanding Large Language Models
  13. 约翰霍普金斯 CS 601.471/671 NLP: Self-supervised Models
  14. 李宏毅 GenAI课程
  15. openai-cookbook: Examples and guides for using the OpenAI API.
  16. Hands on llms: Learn about LLM, LLMOps, and vector DBS for free by designing, training, and deploying a real-time financial advisor LLM system.
  17. 滑铁卢大学 CS 886: Recent Advances on Foundation Models
  18. Mistral: Getting Started with Mistral
  19. 斯坦福 CS25: Transformers United V4
  20. Coursera: Chatgpt 应用提示工程
  21. LangGPT: Empowering everyone to become a prompt expert!
  22. mistralai-cookbook
  23. Introduction to Generative AI 2024 Spring
  24. build nanoGPT: Video+code lecture on building nanoGPT from scratch.
  25. LLM101n: Let's build a Storyteller.
  26. Knowledge Graphs for RAG
  27. LLMs From Scratch (Datawhale Version)
  28. OpenRAG
  29. 通往AGI之路
  30. Andrej Karpathy - Neural Networks: Zero to Hero
  31. Interactive visualization of Transformer
  32. andysingal/llm-course
  33. LM-class
  34. Google Advanced: Generative AI for Developers Learning Path
  35. Anthropics：Prompt Engineering Interactive Tutorial
  36. LLMsBook
  37. Large Language Model Agents
  38. Cohere LLM University
  39. LLMs and Transformers
  40. Smol Vision: Recipes for shrinking, optimizing, customizing cutting edge vision models.
  41. Multimodal RAG: Chat with Videos
  42. LLMs Interview Note
  43. RAG++ : From POC to production: Advanced RAG course.
  44. Weights & Biases AI Academy: Finetuning, building with LLMs, Structured outputs and more LLM courses.
  45. Prompt Engineering & AI tutorials & Resources
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教程 Tutorial

  1. 动手学大模型应用开发
  2. AI开发者频道
  3. B站：五里墩茶社
  4. B站：木羽Cheney
  5. YTB：AI Anytime
  6. B站：漆妮妮
  7. Prompt Engineering Guide
  8. YTB: AI超元域
  9. B站：TechBeat人工智能社区
  10. B站：黄益贺
  11. B站：深度学习自然语言处理
  12. LLM Visualization
  13. 知乎: 原石人类
  14. B站：小黑黑讲AI
  15. B站：面壁的车辆工程师
  16. B站：AI老兵文哲
  17. Large Language Models (LLMs) with Colab notebooks
  18. YTB：IBM Technology
  19. YTB: Unify Reading Paper Group
  20. Chip Huyen
  21. How Much VRAM
  22. Blog: 科学空间（苏剑林）
  23. YTB: Hyung Won Chung
  24. Blog: Tejaswi kashyap
  25. Blog: 小昇的博客
  26. 知乎: ybq
  27. W&B articles
  28. Huggingface Blog
  29. Blog: GbyAI
  30. Blog: mlabonne
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论文 Paper

[!NOTE] 🤝Huggingface Daily Papers、Cool Papers、ML Papers Explained

  1. Hermes-3-Technical-Report
  2. The Llama 3 Herd of Models
  3. Qwen Technical Report
  4. Qwen2 Technical Report
  5. Qwen2-vl Technical Report
  6. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
  7. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
  8. Baichuan 2: Open Large-scale Language Models
  9. DataComp-LM: In search of the next generation of training sets for language models
  10. OLMo: Accelerating the Science of Language Models
  11. MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
  12. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
  13. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
  14. Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
  15. Jamba: A Hybrid Transformer-Mamba Language Model
  16. Textbooks Are All You Need
  17. Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models data
  18. OLMoE: Open Mixture-of-Experts Language Models
  19. Model Merging Paper
  20. Baichuan-Omni Technical Report
  21. 1.5-Pints Technical Report: Pretraining in Days, Not Months – Your Language Model Thrives on Quality Data
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Tips

  1. What We Learned from a Year of Building with LLMs (Part I)
  2. What We Learned from a Year of Building with LLMs (Part II)
  3. What We Learned from a Year of Building with LLMs (Part III): Strategy
  4. 轻松入门大语言模型（LLM）
  5. LLMs for Text Classification: A Guide to Supervised Learning
  6. Unsupervised Text Classification: Categorize Natural Language With LLMs
  7. Text Classification With LLMs: A Roundup of the Best Methods
  8. LLM Pricing
  9. Uncensor any LLM with abliteration
  10. Tiny LLM Universe
  11. Zero-Chatgpt
  12. Zero-Qwen-VL
  13. finetune-Qwen2-VL
  14. MPP-LLaVA
  15. build_MiniLLM_from_scratch
  16. Tiny LLM zh
  17. MiniMind: 3小时完全从0训练一个仅有26M的小参数GPT，最低仅需2G显卡即可推理训练.
  18. LLM-Travel: 致力于深入理解、探讨以及实现与大模型相关的各种技术、原理和应用
  19. Knowledge distillation: Teaching LLM's with synthetic data
  20. Part 1: Methods for adapting large language models
  21. Part 2: To fine-tune or not to fine-tune
  22. Part 3: How to fine-tune: Focus on effective datasets
  23. Reader-LM: Small Language Models for Cleaning and Converting HTML to Markdown
  24. LLMs应用构建一年之心得
  25. LLM训练-pretrain
  26. pytorch-llama: LLaMA 2 implemented from scratch in PyTorch.
  27. Preference Optimization for Vision Language Models with TRL 【support model】
  28. Fine-tuning visual language models using SFTTrainer 【docs】
  29. A Visual Guide to Mixture of Experts (MoE)
  30. Role-Playing in Large Language Models like ChatGPT
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如果你觉得本项目对你有帮助，欢迎引用：

@misc{wang2024llm,
      title={awesome-LLM-resourses}, 
      author={Rongsheng Wang},
      year={2024},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/WangRongsheng/awesome-LLM-resourses}},
}

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This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

github
: 4.5k
vision-llms-are-blind Screenshot
vision-llms-are-blind

This repository contains the code and data for the paper 'Vision Language Models Are Blind'. It explores the limitations of large language models with vision capabilities (VLMs) in performing basic visual tasks that are easy for humans. The repository presents benchmark results showcasing the poor performance of state-of-the-art VLMs on tasks like counting line intersections, identifying circles, letters, and shapes, and following color-coded paths. The research highlights the challenges faced by VLMs in understanding visual information accurately, drawing parallels to myopia and blindness in human vision.

github
: 96
awesome-llm-json Screenshot
awesome-llm-json

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

github
: 1.9k
awesome-llms-fine-tuning Screenshot
awesome-llms-fine-tuning

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

github
: 119
babilong Screenshot
babilong

BABILong is a generative benchmark designed to evaluate the performance of NLP models in processing long documents with distributed facts. It consists of 20 tasks that simulate interactions between characters and objects in various locations, requiring models to distinguish important information from irrelevant details. The tasks vary in complexity and reasoning aspects, with test samples potentially containing millions of tokens. The benchmark aims to challenge and assess the capabilities of Large Language Models (LLMs) in handling complex, long-context information.

github
: 125
Geoweaver Screenshot
Geoweaver

Geoweaver is an in-browser software that enables users to easily compose and execute full-stack data processing workflows using online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides server management, code repository, workflow orchestration software, and history recording capabilities. Users can run it from both local and remote machines. Geoweaver aims to make data processing workflows manageable for non-coder scientists and preserve model run history. It offers features like progress storage, organization, SSH connection to external servers, and a web UI with Python support.

github
: 78
AI.Labs Screenshot
AI.Labs

AI.Labs is an open-source project that integrates advanced artificial intelligence technologies to create a powerful AI platform. It focuses on integrating AI services like large language models, speech recognition, and speech synthesis for functionalities such as dialogue, voice interaction, and meeting transcription. The project also includes features like a large language model dialogue system, speech recognition for meeting transcription, speech-to-text voice synthesis, integration of translation and chat, and uses technologies like C#, .Net, SQLite database, XAF, OpenAI API, TTS, and STT.

github
: 67
awesome-RK3588 Screenshot
awesome-RK3588

RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.

github
: 106
SEED-Bench Screenshot
SEED-Bench

SEED-Bench is a comprehensive benchmark for evaluating the performance of multimodal large language models (LLMs) on a wide range of tasks that require both text and image understanding. It consists of two versions: SEED-Bench-1 and SEED-Bench-2. SEED-Bench-1 focuses on evaluating the spatial and temporal understanding of LLMs, while SEED-Bench-2 extends the evaluation to include text and image generation tasks. Both versions of SEED-Bench provide a diverse set of tasks that cover different aspects of multimodal understanding, making it a valuable tool for researchers and practitioners working on LLMs.

github
: 240
aws-genai-llm-chatbot Screenshot
aws-genai-llm-chatbot

This repository provides code to deploy a chatbot powered by Multi-Model and Multi-RAG using AWS CDK on AWS. Users can experiment with various Large Language Models and Multimodal Language Models from different providers. The solution supports Amazon Bedrock, Amazon SageMaker self-hosted models, and third-party providers via API. It also offers additional resources like AWS Generative AI CDK Constructs and Project Lakechain for building generative AI solutions and document processing. The roadmap and authors are listed, along with contributors. The library is licensed under the MIT-0 License with information on changelog, code of conduct, and contributing guidelines. A legal disclaimer advises users to conduct their own assessment before using the content for production purposes.

github
: 1.1k
HEC-Commander Screenshot
HEC-Commander

HEC-Commander Tools is a suite of python notebooks developed with AI assistance for water resource engineering workflows, providing automation for HEC-RAS and HEC-HMS through Jupyter Notebooks. It contains automation scripts for HEC-HMS, HEC-RAS, and DSS, along with miscellaneous tools. The repository also includes blog posts, ChatGPT assistants, and presentations related to H&H modeling and water resources workflows. Developed to support Region 4 of the Louisiana Watershed Initiative by Fenstermaker.

github
: 53
AlgoListed Screenshot
AlgoListed

Algolisted is a pioneering platform dedicated to algorithmic problem-solving, offering a centralized hub for a diverse array of algorithmic challenges. It provides an immersive online environment for programmers to enhance their skills through Data Structures and Algorithms (DSA) sheets, academic progress tracking, resume refinement with OpenAI integration, adaptive testing, and job opportunity listings. The project is built on the MERN stack, Flask, Beautiful Soup, and Selenium,GEN AI, and deployed on Firebase. Algolisted aims to be a reliable companion in the pursuit of coding knowledge and proficiency.

github
: 134
ClickHouse Screenshot
ClickHouse

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real-time. It offers quick high-level overview, tutorials, documentation, video content, real-time chat support, and various events for users. The tool is designed for real-time analytics and data reporting tasks, providing a scalable and efficient solution for managing analytical data.

github
: 37.2k

For similar tasks

agentcloud Screenshot
agentcloud

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

github
: 500
zep-python Screenshot
zep-python

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

github
: 60
lollms Screenshot
lollms

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

github
: 266
LlamaIndexTS Screenshot
LlamaIndexTS

LlamaIndex.TS is a data framework for your LLM application. Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

github
: 1.8k
semantic-kernel Screenshot
semantic-kernel

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

github
: 21.7k
botpress Screenshot
botpress

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

github
: 12.7k
BotSharp Screenshot
BotSharp

BotSharp is an open-source machine learning framework for building AI bot platforms. It provides a comprehensive set of tools and components for developing and deploying intelligent virtual assistants. BotSharp is designed to be modular and extensible, allowing developers to easily integrate it with their existing systems and applications. With BotSharp, you can quickly and easily create AI-powered chatbots, virtual assistants, and other conversational AI applications.

github
: 2.2k
qdrant Screenshot
qdrant

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

github
: 20.2k

For similar jobs

Awesome-LLM-RAG-Application Screenshot
Awesome-LLM-RAG-Application

Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

github
: 687
ChatGPT-On-CS Screenshot
ChatGPT-On-CS

ChatGPT-On-CS is an intelligent chatbot tool based on large models, supporting various platforms like WeChat, Taobao, Bilibili, Douyin, Weibo, and more. It can handle text, voice, and image inputs, access external resources through plugins, and customize enterprise AI applications based on proprietary knowledge bases. Users can set custom replies, utilize ChatGPT interface for intelligent responses, send images and binary files, and create personalized chatbots using knowledge base files. The tool also features platform-specific plugin systems for accessing external resources and supports enterprise AI applications customization.

github
: 2.2k
call-gpt Screenshot
call-gpt

Call GPT is a voice application that utilizes Deepgram for Speech to Text, elevenlabs for Text to Speech, and OpenAI for GPT prompt completion. It allows users to chat with ChatGPT on the phone, providing better transcription, understanding, and speaking capabilities than traditional IVR systems. The app returns responses with low latency, allows user interruptions, maintains chat history, and enables GPT to call external tools. It coordinates data flow between Deepgram, OpenAI, ElevenLabs, and Twilio Media Streams, enhancing voice interactions.

github
: 127
awesome-LLM-resourses Screenshot
awesome-LLM-resourses

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

github
: 1.7k
tappas Screenshot
tappas

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

github
: 78
cloudflare-rag Screenshot
cloudflare-rag

This repository provides a fullstack example of building a Retrieval Augmented Generation (RAG) app with Cloudflare. It utilizes Cloudflare Workers, Pages, D1, KV, R2, AI Gateway, and Workers AI. The app features streaming interactions to the UI, hybrid RAG with Full-Text Search and Vector Search, switchable providers using AI Gateway, per-IP rate limiting with Cloudflare's KV, OCR within Cloudflare Worker, and Smart Placement for workload optimization. The development setup requires Node, pnpm, and wrangler CLI, along with setting up necessary primitives and API keys. Deployment involves setting up secrets and deploying the app to Cloudflare Pages. The project implements a Hybrid Search RAG approach combining Full Text Search against D1 and Hybrid Search with embeddings against Vectorize to enhance context for the LLM.

github
: 93
pixeltable Screenshot
pixeltable

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

github
: 59
wave-apps Screenshot
wave-apps

Wave Apps is a directory of sample applications built on H2O Wave, allowing users to build AI apps faster. The apps cover various use cases such as explainable hotel ratings, human-in-the-loop credit risk assessment, mitigating churn risk, online shopping recommendations, and sales forecasting EDA. Users can download, modify, and integrate these sample apps into their own projects to learn about app development and AI model deployment.

github
: 145