privateGPT
h2ogpt
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| privateGPT | h2ogpt | |
|---|---|---|
| 1 | 28 | |
| 50,198 | 10,327 | |
| - | 4.7% | |
| - | 10.0 | |
| 28 days ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
privateGPT
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PrivateGPT exploring the Documentation
# install developer tools xcode-select --install # create python sandbox mkdir PrivateGTP cd privateGTP/ python3 -m venv . # actiavte local context source bin/activate # privateGTP uses poetry for python module management privateGTP> pip install poetry # sync privateGTP project privateGTP> git clone https://github.com/imartinez/privateGPT # enable MPS for model loading and processing privateGTP> CMAKE_ARGS="-DLLAMA_METAL=on" pip install --force-reinstall --no-cache-dir llama-cpp-python privateGTP> cd privateGPT # Import configure python dependencies privateGTP> poetry run python3 scripts/setup # launch web interface to confirm operational on default model privateGTP> python3 -m private_gpt # navigate safari browser to http://localhost:8001/ # To bulk import documentation needed to stop the web interface as vector database not in multi-user mode privateGTP> [control] + "C" # import some PDFs privateGTP> curl "https://docs.intersystems.com/irislatest/csp/docbook/pdfs.zip" -o /tmp/pdfs.zip privateGTP> unzip /tmp/pdfs.zip -d /tmp # took a few hours to process privateGTP> make ingest /tmp/pdfs/pdfs/ # launch web interface again for query documentation privateGTP> python3 -m private_gpt
h2ogpt
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
As others have said you want RAG.
The most feature complete implementation I've seen is h2ogpt[0] (not affiliated).
The code is kind of a mess (most of the logic is in an ~8000 line python file) but it supports ingestion of everything from YouTube videos to docx, pdf, etc - either offline or from the web interface. It uses langchain and a ton of additional open source libraries under the hood. It can run directly on Linux, via docker, or with one-click installers for Mac and Windows.
It has various model hosting implementations built in - transformers, exllama, llama.cpp as well as support for model serving frameworks like vLLM, HF TGI, etc or just OpenAI.
You can also define your preferred embedding model along with various other parameters but I've found the out of box defaults to be pretty sane and usable.
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chatgpt alternative
Here's the links: https://github.com/h2oai/h2ogpt/blob https://github.com/h2oai/h2ogpt/blob/main/docs/README_LangChain.md
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Went down the rabbit hole of 100% local RAG, it works but are there better options?
Take a look at h2ogpt. It's open and local with API (incoming and outgoing) and impressive feature set, including RAG from docs, images, and web search.
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[SEEKING ADVICE] Looking for Existing Repos (Open-Source, VM-Hosted, & GPU-Compatible)
I've stumbled upon h2ogptas a potential starting point. Are there better solutions or repositories that can meet these requirements?
- H2Oai GPT CPU
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Open source Q&A chatbot UI Recommendation?
Any recommendations for an open source repos that support web based chat ui where you can upload docs,pds,links,etc? So far i found https://github.com/openchatai/OpenChat but it doesnt support llama, claude, etc. Theres also https://github.com/h2oai/h2ogpt but their gradio UI is overly complicated (meant for technical people) and not user friendly.
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My experience on starting with fine tuning LLMs with custom data
I'm also working on the finetuning of models for Q&A and I've finetuned llama-7b, falcon-40b, and oasst-pythia-12b using HuggingFace's SFT, H2OGPT's finetuning script and lit-gpt.
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H2O.ai Introduces h2oGPT: A Suite of Open-Source Code Repositories for Democratizing Large Language Models (LLMs)
Github link: https://github.com/h2oai/h2ogpt
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tokenizers error is driving me nuts
quite a number of AI tools written in Python do not work for me and usually because of the same error: RuntimeError: Failed to import transformers.models.auto because of the following error (look up to see its traceback): No module named 'tokenizers.tokenizers' This time it's https://github.com/h2oai/h2ogpt
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LLM for PDFs
privateGPT or h2ogpt
What are some alternatives?
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
anything-llm - The all-in-one AI application, tool suite, and API for RAG & Agents for Docker & Desktop.
llama_index - LlamaIndex is a data framework for your LLM applications
gpt4all - gpt4all: run open-source LLMs anywhere
ollama - Get up and running with Llama 2, Mistral, Gemma, and other large language models.
local_llama - This repo is to showcase how you can run a model locally and offline, free of OpenAI dependencies.
langchain - 🦜🔗 Build context-aware reasoning applications
h2o-llmstudio - H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/
danswer - Gen-AI Chat for Teams - Think ChatGPT if it had access to your team's unique knowledge.
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.