h2oGPT

Name: h2ogpt
Author: h2oai

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Query and summarize your documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project.

Check out a long CoT Open-o1 open 🍓strawberry🍓 project: https://github.com/pseudotensor/open-strawberry

Try Enterprise Version for Free

Enterprise h2oGPTe

Video Demo

https://github.com/h2oai/h2ogpt/assets/2249614/2f805035-2c85-42fb-807f-fd0bca79abc6

img-small.png YouTube 4K Video

Features

Private offline database of any documents (PDFs, Excel, Word, Images, Video Frames, YouTube, Audio, Code, Text, MarkDown, etc.)
Persistent database (Chroma, Weaviate, or in-memory FAISS) using accurate embeddings (instructor-large, all-MiniLM-L6-v2, etc.)
Efficient use of context using instruct-tuned LLMs (no need for LangChain's few-shot approach)
Parallel summarization and extraction, reaching an output of 80 tokens per second with the 13B LLaMa2 model
HYDE (Hypothetical Document Embeddings) for enhanced retrieval based upon LLM responses
Semantic Chunking for better document splitting (requires GPU)
Variety of models supported (LLaMa2, Mistral, Falcon, Vicuna, WizardLM. With AutoGPTQ, 4-bit/8-bit, LORA, etc.)
GPU support from HF and LLaMa.cpp GGML models, and CPU support using HF, LLaMa.cpp, and GPT4ALL models
Attention Sinks for arbitrarily long generation (LLaMa-2, Mistral, MPT, Pythia, Falcon, etc.)
Gradio UI or CLI with streaming of all models
Upload and View documents through the UI (control multiple collaborative or personal collections)
Vision Models LLaVa, Claude-3, Gemini-Pro-Vision, GPT-4-Vision
Image Generation Stable Diffusion (sdxl-turbo, sdxl, SD3), PlaygroundAI (playv2), and Flux
Voice STT using Whisper with streaming audio conversion
Voice TTS using MIT-Licensed Microsoft Speech T5 with multiple voices and Streaming audio conversion
Voice TTS using MPL2-Licensed TTS including Voice Cloning and Streaming audio conversion
AI Assistant Voice Control Mode for hands-free control of h2oGPT chat
Bake-off UI mode against many models at the same time
Easy Download of model artifacts and control over models like LLaMa.cpp through the UI
Authentication in the UI by user/password via Native or Google OAuth
State Preservation in the UI by user/password
Open Web UI with h2oGPT as backend via OpenAI Proxy
See Start-up Docs.
Chat completion with streaming
Document Q/A using h2oGPT ingestion with advanced OCR from DocTR
Vision models
Audio Transcription (STT)
Audio Generation (TTS)
Image generation
Authentication
State preservation
Linux, Docker, macOS, and Windows support
Inference Servers support for oLLaMa, HF TGI server, vLLM, Gradio, ExLLaMa, Replicate, Together.ai, OpenAI, Azure OpenAI, Anthropic, MistralAI, Google, and Groq
OpenAI compliant
Server Proxy API (h2oGPT acts as drop-in-replacement to OpenAI server)
Chat and Text Completions (streaming and non-streaming)
Audio Transcription (STT)
Audio Generation (TTS)
Image Generation
Embedding
Function tool calling w/auto tool selection
AutoGen Code Execution Agent
JSON Mode
Strict schema control for vLLM via its use of outlines
Strict schema control for OpenAI, Anthropic, Google Gemini, MistralAI models
JSON mode for some older OpenAI or Gemini models with schema control if model is smart enough (e.g. gemini 1.5 flash)
Any model via code block extraction
Web-Search integration with Chat and Document Q/A
Agents for Search, Document Q/A, Python Code, CSV frames
High quality Agents via OpenAI proxy server on separate port
Code-first agent that generates plots, researches, evaluates images via vision model, etc. (client code openai_server/openai_client.py).
No UI for this, just API
Evaluate performance using reward models
Quality maintained with over 1000 unit and integration tests taking over 24 GPU-hours

Get Started

Install h2oGPT

Docker is recommended for Linux, Windows, and MAC for full capabilities. Linux Script also has full capability, while Windows and MAC scripts have less capabilities than using Docker.

Collab Demos

Resources

Docs Guide

Development

To create a development environment for training and generation, follow the installation instructions.
To fine-tune any LLM models on your data, follow the fine-tuning instructions.
To run h2oGPT tests: bash pip install requirements-parser pytest-instafail pytest-random-order playsound==1.3.0 conda install -c conda-forge gst-python -y sudo apt-get install gstreamer-1.0 pip install pygame GPT_H2O_AI=0 CONCURRENCY_COUNT=1 pytest --instafail -s -v tests # for openai server test on already-running local server pytest -s -v -n 4 openai_server/test_openai_server.py::test_openai_client or tweak/run tests/test4gpus.sh to run tests in parallel.

Acknowledgements

Some training code was based upon March 24 version of Alpaca-LoRA.
Used high-quality created data by OpenAssistant.
Used base models by EleutherAI.
Used OIG data created by LAION.

Why H2O.ai?

Our Makers at H2O.ai have built several world-class Machine Learning, Deep Learning and AI platforms: - #1 open-source machine learning platform for the enterprise H2O-3 - The world's best AutoML (Automatic Machine Learning) with H2O Driverless AI - No-Code Deep Learning with H2O Hydrogen Torch - Document Processing with Deep Learning in Document AI

We also built platforms for deployment and monitoring, and for data wrangling and governance: - H2O MLOps to deploy and monitor models at scale - H2O Feature Store in collaboration with AT&T - Open-source Low-Code AI App Development Frameworks Wave and Nitro - Open-source Python datatable (the engine for H2O Driverless AI feature engineering)

Many of our customers are creating models and deploying them enterprise-wide and at scale in the H2O AI Cloud: - Multi-Cloud or on Premises - Managed Cloud (SaaS) - Hybrid Cloud - AI Appstore

We are proud to have over 25 (of the world's 280) Kaggle Grandmasters call H2O home, including three Kaggle Grandmasters who have made it to world #1.

Disclaimer

Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.

Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.

By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

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