embedchain
h2ogpt
| embedchain | h2ogpt | |
|---|---|---|
| 6 | 28 | |
| 8,541 | 10,506 | |
| 2.3% | 3.4% | |
| 9.8 | 10.0 | |
| 6 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.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
embedchain
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.
The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.
[1]: https://github.com/embedchain/embedchain
- Embedchain
- Framework to easily create LLM powered bots over any dataset
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[D] Hardest thing about building with LLMs?
Langchain is a big wrapper in itself and people can't be bothered to even use that to write 10 lines of code. Look at the traction this project is getting https://github.com/embedchain/embedchain, at it's heart it's just using few modules from langchain. The whole thing, chunking+embedding+retrieval+promoting can be done in 100 lines without langchain and embedchain.
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AI — weekly megathread!
Embedchain: a framework to easily create LLM powered bots over any dataset [Link].
- EmbedChain: Framework to easily create LLM powered bots over any dataset.
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.
[0] - https://github.com/h2oai/h2ogpt
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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?
trulens - Evaluation and Tracking for LLM Experiments
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
HeimdaLLM - Constrain LLM output
privateGPT - Interact with your documents using the power of GPT, 100% privately, no data leaks [Moved to: https://github.com/zylon-ai/private-gpt]
WebGLM - WebGLM: An Efficient Web-enhanced Question Answering System (KDD 2023)
llama_index - LlamaIndex is a data framework for your LLM applications
openchat - OpenChat: Advancing Open-source Language Models with Imperfect Data
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
gpt-migrate - Easily migrate your codebase from one framework or language to another.
local_llama - This repo is to showcase how you can run a model locally and offline, free of OpenAI dependencies.
searchGPT - Grounded search engine (i.e. with source reference) based on LLM / ChatGPT / OpenAI API. It supports web search, file content search etc.
h2o-llmstudio - H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/