private-gpt
BrainChulo
private-gpt | BrainChulo | |
---|---|---|
131 | 10 | |
52,412 | 142 | |
3.6% | 2.1% | |
9.2 | 9.0 | |
3 days ago | 8 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
private-gpt
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.
https://github.com/zylon-ai/private-gpt
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PrivateGPT exploring the Documentation
Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion
- Show HN: I made an app to use local AI as daily driver
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privateGPT VS quivr - a user suggested alternative
2 projects | 12 Jan 2024
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Run https://github.com/imartinez/privateGPT
Then
make ingest /path/to/folder/with/files
Then chat to the LLM.
Done.
Docs: https://docs.privategpt.dev/overview/welcome/quickstart
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Mozilla "MemoryCache" Local AI
PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.
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What Is Retrieval-Augmented Generation a.k.a. RAG
I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].
The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.
[0] https://gpt4all.io/index.html
[1] https://www.danswer.ai/
[2] https://github.com/imartinez/privateGPT
- LM Studio – Discover, download, and run local LLMs
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Ask HN: Local LLM Recommendation?
https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...
https://github.com/imartinez/privateGPT
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Run ChatGPT-like LLMs on your laptop in 3 lines of code
I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.
This resulted in Python package I call OnPrem.LLM.
In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/
Enjoy!
BrainChulo
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Alternative to LangChain for open LLMs?
On BrainChulo, we’re going 100% guidance mode, see for instance an implementation of Chain of Thoughts on top of a thin guidance wrapper: https://github.com/ChuloAI/BrainChulo/blob/main/app/guidance_tooling/guidance_agent/agent.py
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Running local LLM for info retrieval of technical documents
Awesome resource! If I may suggest that you'd add one, some friends and I are working on data retrieval with llm project as well, with our differentiating marker being that we are trying to implement guidance in order to improve the agent efficiency. If you guys wanna take a look :) https://github.com/ChuloAI/BrainChulo
- LlamaCPP and LangChain Agent Quality
- Training a 13B LLaMA on information from documents.
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Chat with Documents using Open source LLMs
Plug: https://github.com/iGavroche/BrainChulo - BrainChulo currently works on top of Ooba but uses its own UI interface. Its first goal is to provide a production-level way to do Retrieveal Augmentation on Open Source LLMs via vector stores and good prompt engineering.
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What features would everyone like to see in oog?
Regarding this, I've joined a project that is doing some nice progress on this front. Still WIP but we're getting there, checkout BrainChulo :)
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7B models use with Langchainn for Chatbox importing of txt or pdf's
This is exactly what BrainChulo aims to do. You should check it out: https://github.com/CryptoRUSHGav/BrainChulo/ and feel free to drop on the discord to give us your feedback, your use-case, or if you need help getting started.
- [Local Llama] Aggiunta di memoria a lungo termine a LLM personalizzati: domiamo Vicuna insieme!
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adding models to oobabooga
The download script is broken. I posted a working version on my repo: https://github.com/CryptoRUSHGav/BrainChulo
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Adding Long-Term Memory to Custom LLMs: Let's Tame Vicuna Together!
I'm hoping that many of you brilliant people can join me in our common quest to add long-term memory to our favorite camelid, Vicuna. The repository is called BrainChulo, and it's just waiting for your contributions.
What are some alternatives?
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
gpt4all - gpt4all: run open-source LLMs anywhere
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
outlines - Structured Text Generation
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
long_term_memory - A gradio web UI for running Large Language Models like GPT-J 6B, OPT, GALACTICA, LLaMA, and Pygmalion.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
gpt-llama.cpp - A llama.cpp drop-in replacement for OpenAI's GPT endpoints, allowing GPT-powered apps to run off local llama.cpp models instead of OpenAI.
llama.cpp - LLM inference in C/C++
ChatALL - Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, Claude, ChatGLM, MOSS, 讯飞星火, 文心一言 and more, discover the best answers