Difference between revisions of "AI tools"
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* 2025-01Jan-02: [https://huggingface.co/PowerInfer/SmallThinker-3B-Preview SmallThinker-3B-Preview] (fine-tune of [https://huggingface.co/Qwen/Qwen2.5-3B-Instruct Qwen2.5-3b-Instruct]) | * 2025-01Jan-02: [https://huggingface.co/PowerInfer/SmallThinker-3B-Preview SmallThinker-3B-Preview] (fine-tune of [https://huggingface.co/Qwen/Qwen2.5-3B-Instruct Qwen2.5-3b-Instruct]) | ||
* [https://x.com/SebastienBubeck/status/1877010995727470877 2025-01Jan-08]: Microsoft [https://huggingface.co/microsoft/phi-4 phi-4] 15B | * [https://x.com/SebastienBubeck/status/1877010995727470877 2025-01Jan-08]: Microsoft [https://huggingface.co/microsoft/phi-4 phi-4] 15B | ||
+ | * [https://x.com/MiniMax__AI/status/1879226391352549451 2025-01Jan-14]: [https://www.minimaxi.com/en/news/minimax-01-series-2 MiniMax-01], MiniMax-Text-01 and MiniMax-VL-01; 4M context length ([https://www.minimaxi.com/en/news/minimax-01-series-2 paper]) | ||
===For Coding=== | ===For Coding=== | ||
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* 2024-12Dec-24: Qwen [https://huggingface.co/Qwen/QVQ-72B-Preview QvQ-72B-preview] (visual reasoning) | * 2024-12Dec-24: Qwen [https://huggingface.co/Qwen/QVQ-72B-Preview QvQ-72B-preview] (visual reasoning) | ||
* 2025-01Jan-10: [https://mbzuai-oryx.github.io/LlamaV-o1/ LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs] ([https://arxiv.org/abs/2501.06186 preprint], [https://github.com/mbzuai-oryx/LlamaV-o1 code], [https://huggingface.co/omkarthawakar/LlamaV-o1 weights]) | * 2025-01Jan-10: [https://mbzuai-oryx.github.io/LlamaV-o1/ LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs] ([https://arxiv.org/abs/2501.06186 preprint], [https://github.com/mbzuai-oryx/LlamaV-o1 code], [https://huggingface.co/omkarthawakar/LlamaV-o1 weights]) | ||
+ | * [https://x.com/deepseek_ai/status/1881318130334814301 2025-01Jan-20]: [https://huggingface.co/deepseek-ai/DeepSeek-R1 DeepSeek-R1], [https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B DeepSeek-R1-Distill-Llama-70B], DeepSeek-R1-Distill-Qwen-32B, ... ([https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf paper]) | ||
==Cloud LLM== | ==Cloud LLM== | ||
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* 2024-09: [https://arxiv.org/abs/2409.14924 Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely] | * 2024-09: [https://arxiv.org/abs/2409.14924 Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely] | ||
* 2024-12: [https://arxiv.org/abs/2412.17558 A Survey of Query Optimization in Large Language Models] | * 2024-12: [https://arxiv.org/abs/2412.17558 A Survey of Query Optimization in Large Language Models] | ||
+ | * 2025-01: [https://arxiv.org/abs/2501.07391 Enhancing Retrieval-Augmented Generation: A Study of Best Practices] | ||
* List of [https://github.com/NirDiamant/RAG_Techniques RAG techniques] | * List of [https://github.com/NirDiamant/RAG_Techniques RAG techniques] | ||
* [https://github.com/athina-ai/rag-cookbooks Advanced RAG Cookbooks👨🏻💻] | * [https://github.com/athina-ai/rag-cookbooks Advanced RAG Cookbooks👨🏻💻] | ||
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* 2025-01: [https://arxiv.org/abs/2501.05366 Search-o1: Agentic Search-Enhanced Large Reasoning Models] ([https://search-o1.github.io/ project], [https://github.com/sunnynexus/Search-o1 code]) | * 2025-01: [https://arxiv.org/abs/2501.05366 Search-o1: Agentic Search-Enhanced Large Reasoning Models] ([https://search-o1.github.io/ project], [https://github.com/sunnynexus/Search-o1 code]) | ||
* 2025-01: [https://github.com/Marker-Inc-Korea/AutoRAG AutoRAG: RAG AutoML tool for automatically finding an optimal RAG pipeline for your data] | * 2025-01: [https://github.com/Marker-Inc-Korea/AutoRAG AutoRAG: RAG AutoML tool for automatically finding an optimal RAG pipeline for your data] | ||
+ | * 2025-01: [https://arxiv.org/abs/2501.05874 VideoRAG: Retrieval-Augmented Generation over Video Corpus] | ||
===Open-source Implementations=== | ===Open-source Implementations=== | ||
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* [https://github.com/DS4SD/docling Docling]: converts multiple formats (PDF, DOCX, PPTX, Images, HTML) into Markdown and JSON | * [https://github.com/DS4SD/docling Docling]: converts multiple formats (PDF, DOCX, PPTX, Images, HTML) into Markdown and JSON | ||
* [https://github.com/microsoft/markitdown Microsoft Markitdown]: converts various formats (PDF, Word, Excel, PPT) to Markdown (available via [https://msftmd.replit.app/ web interface on replit]) | * [https://github.com/microsoft/markitdown Microsoft Markitdown]: converts various formats (PDF, Word, Excel, PPT) to Markdown (available via [https://msftmd.replit.app/ web interface on replit]) | ||
+ | * [https://github.com/wisupai/e2m e2m: Everything to Markdown] (doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a) | ||
+ | * Nvidia [https://docs.nvidia.com/nv-ingest/user-guide/index.html NV-ingest] ([https://github.com/NVIDIA/nv-ingest code]) scalable, performance-oriented document content and metadata extraction microservice | ||
+ | * [https://github.com/QuivrHQ/MegaParse MegaParse]: Your Parser for every type of documents (pdf, powerpoint, word) | ||
+ | |||
====PDF Conversion==== | ====PDF Conversion==== | ||
* [https://github.com/kermitt2/grobid Grobid] | * [https://github.com/kermitt2/grobid Grobid] | ||
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* [https://cartesia.ai/ Cartesia] [https://cartesia.ai/sonic Sonic] | * [https://cartesia.ai/ Cartesia] [https://cartesia.ai/sonic Sonic] | ||
* [https://neets.ai/ Neets AI] ($1/million characters) | * [https://neets.ai/ Neets AI] ($1/million characters) | ||
+ | * Hailuo AI T2A-01-HD ([https://www.hailuo.ai/audio try], [https://intl.minimaxi.com/document/platform%20introduction?key=66701c8e1d57f38758d58198 API]) | ||
=Text-to-audio= | =Text-to-audio= | ||
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* [https://github.com/unclecode/crawl4ai Crawl4AI: Crawl Smarter, Faster, Freely. For AI.] | * [https://github.com/unclecode/crawl4ai Crawl4AI: Crawl Smarter, Faster, Freely. For AI.] | ||
* [https://github.com/ScrapeGraphAI/Scrapegraph-ai ScrapeGraphAI: You Only Scrape Once]: web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.) | * [https://github.com/ScrapeGraphAI/Scrapegraph-ai ScrapeGraphAI: You Only Scrape Once]: web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.) | ||
+ | ===Headless Browser (scrape & automate)=== | ||
+ | * [https://github.com/lightpanda-io/browser Lightpanda Browser] | ||
+ | |||
===Github=== | ===Github=== | ||
* [https://github.com/cyclotruc/gitingest GitIngest]: Turn any GitHub repository into a prompt-friendly text file, for inclusion in LLM's context. Available at: [https://gitingest.com/ gitingest.com] | * [https://github.com/cyclotruc/gitingest GitIngest]: Turn any GitHub repository into a prompt-friendly text file, for inclusion in LLM's context. Available at: [https://gitingest.com/ gitingest.com] |
Latest revision as of 10:34, 20 January 2025
Contents
- 1 LLM
- 2 LLM Agents
- 3 Interfaces
- 4 Speech Recognition (ASR) and Transcription
- 5 Text-to-speech (TTS)
- 6 Text-to-audio
- 7 Vision
- 8 Embedding
- 9 Time Series
- 10 Data
- 11 See Also
LLM
Open-weights LLM
- 2023-07Jul-18: Llama2 7B, 13B, 70B
- 2024-04Apr-18: Llama3 8B, 70B
- 2024-06Jun-14: Nemotron-4 340B
- 2024-07Jul-23: Llama 3.1 8B, 70B, 405B
- 2024-07Jul-24: Mistral Large 2 128B
- 2024-07Jul-31: Gemma 2 2B
- 2024-08Aug-08: Qwen2-Math (hf, github) 1.5B, 7B, 72B
- 2024-08Aug-14: Nous research Hermes 3 (technical report) 8B, 70B, 405B
- 2024-08Aug-19: Salesforce AI xGen-MM (BLIP-3): A Family of Open Large Multimodal Models (preprint, code)
- 2024-09Sep-04: OLMoE: Open Mixture-of-Experts Language Models (code) 7B model (uses 1B per input token)
- 2024-09Sep-05: Reflection 70B (demo): Trained using Reflection-Tuning, a technique developed to enable LLMs to fix their own mistakes.
- 2024-09Sep-06: DeepSeek-V2.5 238B mixture-of-experts (160 experts, 16B active params)
- 2024-09Sep-19: Microsoft GRadient-INformed (GRIN) MoE (demo, model, github) 6.6B
- 2024-09Sep-23: Nvidia Llama-3_1-Nemotron-51B-instruct 51B
- 2024-09Sep-25: Meta Llama 3.2 with visual and voice modalities 1B, 3B, 11B, 90B
- 2024-09Sep-25: Ai2 Molmo multi-modal models 1B, 7B, 72B
- 2024-10Oct-01: Nvidia NVLM-D-72B (includes vision)
- 2024-10Oct-16: Mistral Ministral-8B-Instruct-2410
- 2024-10Oct-16: Nvidia Llama-3.1-Nemotron-70B-Reward
- 2024-11Nov-04: Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent 389B (code, weights)
- 2024-11Nov-18: Mistral-Large-Instruct-2411) 123B; and Pixtral Large multimodal model 124B (weights)
- 2024-11Nov-22: Nvidia Hymba (blog): small and high-performance
- 2024-12Dec-06: Meta Llama 3.3 70B
- 2024-12Dec-26: DeepSeek-V3-Base 671B
- 2025-01Jan-02: SmallThinker-3B-Preview (fine-tune of Qwen2.5-3b-Instruct)
- 2025-01Jan-08: Microsoft phi-4 15B
- 2025-01Jan-14: MiniMax-01, MiniMax-Text-01 and MiniMax-VL-01; 4M context length (paper)
For Coding
Rankings: bigcode-models-leaderboard and CodeElo leaderboard
- 2024-10Oct-06: Abacus AI Dracarys2-72B-Instruct (optimized for coding, fine-tune of Qwen2.5-72B-Instruct)
- 2024-11Nov-09: OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (weights, preprint)
- 2024-11Nov-13: Qwen2.5-Coder
Reasoning
- 2024-11Nov-20: DeepSeek-R1-Lite-Preview (results, CoT)
- 2024-11Nov-23: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
- 2024-11Nov-27: Alibaba Qwen QwQ 32B (model, demo)
- 2024-12Dec-04: Ruliad Deepthought 8B (demo)
- 2024-12Dec-24: Qwen QvQ-72B-preview (visual reasoning)
- 2025-01Jan-10: LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs (preprint, code, weights)
- 2025-01Jan-20: DeepSeek-R1, DeepSeek-R1-Distill-Llama-70B, DeepSeek-R1-Distill-Qwen-32B, ... (paper)
Cloud LLM
Multi-modal: Audio
- kyutai Open Science AI Lab chatbot moshi
Triage
Retrieval Augmented Generation (RAG)
Reviews
- 2024-08: Graph Retrieval-Augmented Generation: A Survey
- 2024-09: Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
- 2024-12: A Survey of Query Optimization in Large Language Models
- 2025-01: Enhancing Retrieval-Augmented Generation: A Study of Best Practices
- List of RAG techniques
- Advanced RAG Cookbooks👨🏻💻
Measuring RAG performance
- 2025-01: The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
Analysis of RAG overall
Approaches
- RAGFlow (code)
- GraphRAG (preprint, code, GraphRAG Accelerator for easy deployment on Azure)
- AutoMetaRAG (code)
- Verba: RAG for Weaviate vector database (code, video)
- 2024-10: Google Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
- 2024-10: StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization: Reformats retrieved data into task-appropriate structures (table, graph, tree).
- 2024-10: Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
- 2024-11: FastRAG: Retrieval Augmented Generation for Semi-structured Data
- 2024-11: Microsoft LazyGraphRAG: Setting a new standard for quality and cost
- 2024-11: Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
- 2025-01: Search-o1: Agentic Search-Enhanced Large Reasoning Models (project, code)
- 2025-01: AutoRAG: RAG AutoML tool for automatically finding an optimal RAG pipeline for your data
- 2025-01: VideoRAG: Retrieval-Augmented Generation over Video Corpus
Open-source Implementations
- kotaemon: An open-source clean & customizable RAG UI for chatting with your documents.
- LlamaIndex (code, docs, voice chat code)
- Nvidia ChatRTX with RAG
- Anthropic Customer Support Agent example
- LangChain and LangGraph (tutorial)
- RAGBuilder: Automatically tunes RAG hyperparams
- WikiChat
- Chonkie: No-nonsense RAG chunking library (open-source, lightweight, fast)
- autoflow: open source GraphRAG (Knowledge Graph), including conversational search page
- RAGLite
- nano-graphrag: A simple, easy-to-hack GraphRAG implementation
- Dabarqus
Web-based Tools
- SciSpace Chat with PDF (also available as a GPT).
Commercial Cloud Offerings
Document Parsing
- Docling: converts multiple formats (PDF, DOCX, PPTX, Images, HTML) into Markdown and JSON
- Microsoft Markitdown: converts various formats (PDF, Word, Excel, PPT) to Markdown (available via web interface on replit)
- e2m: Everything to Markdown (doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a)
- Nvidia NV-ingest (code) scalable, performance-oriented document content and metadata extraction microservice
- MegaParse: Your Parser for every type of documents (pdf, powerpoint, word)
PDF Conversion
Automatic Optimization
Analogous to Gradient Descent
LLM for scoring/ranking
- GPTScore: Evaluate as You Desire
- Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
- Domain-specific chatbots for science using embeddings
- Large Language Models as Evaluators for Scientific Synthesis
LLM Agents
- See AI Agents.
Interfaces
Chatbot Frontend
Web (code)
Web (product)
Desktop GUI
- AnythingLLM (docs, code): includes chat-with-docs, selection of LLM and vector db, etc.
Alternative Text Chatbot UI
- Loom provides a sort of tree-like structure for LLM coming up with branched writings.
- The Pantheon Interface is a new idea for how to interact with LLMs (live instance, code). In a traditional interaction, you prompt the bot and it replies in a turn-by-turn manner. Pantheon instead invites you to type out your thoughts, and various agents will asynchronously add comments or questions to spur along your brainstorming.
Conversational Audio Chatbot
- Swift is a fast AI voice assistant (code, live demo) uses:
- RTVI-AI (code, demo), uses:
- June: Local Voice Chatbot
- Ollama
- Hugging Face Transformers (for speech recognition)
- Coqui TTS Toolkit
- kyutai Moshi chatbot (demo)
- Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming (model, code, demo)
- 2024-09Sep-11: Llama-3.1-8B-Omni (code), enabling end-to-end speech.
- 2024-10Oct-18: Meta Spirit LM: open source multimodal language model that freely mixes text and speech
Related Research
Commercial Systems
Speech Recognition (ASR) and Transcription
Lists
Open Source
- DeepSpeech
- speechbrain
- Kaldi
- wav2vec 2.0
- Whisper
- Whisper medium.en
- WhisperX (includes word-level timestamps and speaker diarization)
- Distil Large v3 with MLX
- 2024-10: whisper-large-v3-turbo distillation (demo, code)
- Nvidia Canary 1B
- 2024-09: Nvidia NeMo
- 2024-10: Rev AI models for transcription and diarization
- 2024-10: Moonshine (optimized for resource-constrained devices)
In Browser
- Whisper Timestamped: Multilingual speech recognition with word-level timestamps, running locally in browser
Phrase Endpointing and Voice Activity Detection (VAD)
I.e. how to determine when user is done talking, and bot should respond?
Audio Cleanup
- Krisp AI: Noise cancellation, meeting summary, etc.
Text-to-speech (TTS)
Open Source
- Parler TTS (demo)
- Toucan (demo)
- MetaVoice (github)
- ChatTTS
- Camb.ai MARS5-TTS
- Coqui TTS Toolkit
- Fish Speech 1.4: multi-lingual, can clone voices (video, weights, demo)
- F5-TTS (demo): cloning, emotion, etc.
- MaskGCT (demo)
- Amphion: An Open-Source Audio, Music and Speech Generation Toolkit (code)
Cloud
- Elevenlabs ($50/million characters)
- Cartesia Sonic
- Neets AI ($1/million characters)
- Hailuo AI T2A-01-HD (try, API)
Text-to-audio
- 2024-12: TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization (code)
Vision
Visual Models
- CLIP
- Siglip
- Supervision
- Florence-2
- Nvidia MambaVision
- Meta Sapiens: Foundation for Human Vision Models (video input, can infer segmentation, pose, depth-map, and surface normals)
Multi-modal Models (language-vision/video)
- LLaVA-NeXT-Interleave (models, demo)
- SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
- Nvidia NVEagle 13B, 7B (demo, preprint)
- 2024-08Aug-29: Qwen2-VL 7B, 2B (code, models): Can process videos up to 20 minutes in length
- 2024-09Sep-11: Mistral Pixtral 12B
- 2024-09Sep-17: NVLM 1.0
- 2024-12Dec-06: Nvidia NVILA: Efficient Frontier Visual Language Models
Optical character recognition (OCR)
- General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model (project, code, demo)
- Swift OCR: LLM Powered Fast OCR
Embedding
Time Series
- Stumpy: Python library, uses near-match subsequences for similarity and forecasting
- Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
- From latent dynamics to meaningful representations
- Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
- A decoder-only foundation model for time-series forecasting
- TimeGPT-1
- Unified Training of Universal Time Series Forecasting Transformers
- xLSTMTime : Long-term Time Series Forecasting With xLSTM
- Salesforce: Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts (code, weights, blog)
Control
Forecasting
- Meta Kats (code): Forecasting (ARIMA, Prophet, Holt Winters, VAR), detection, feature extraction, simulation
- Context is Key: A Benchmark for Forecasting with Essential Textual Information
Data
Vector Database
Open Source
- milvus (open source with paid cloud option)
- Qdrant (open source with paid cloud option)
- Vespa (open source with paid cloud option)
- chroma
- LlamaIndex
- sqlite-vec
Commercial cloud
MySQL
- MySQL does not traditionally have support, but:
- PlanetScale is working on it
- mysql_vss (discussion)
- tibd (discussion)
Database with Search
Web Scraping
- Firecrawl
- Crawl4AI: Crawl Smarter, Faster, Freely. For AI.
- ScrapeGraphAI: You Only Scrape Once: web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.)
Headless Browser (scrape & automate)
Github
- GitIngest: Turn any GitHub repository into a prompt-friendly text file, for inclusion in LLM's context. Available at: gitingest.com
- github.gg: For analyzing GitHub repositories and providing valuable insights about code quality, dependencies, and more
- Flatty - Codebase-to-Text for LLMs