privateGPT
local_llama
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privateGPT | local_llama | |
---|---|---|
1 | 10 | |
50,198 | 178 | |
- | - | |
- | 6.8 | |
about 1 month ago | 6 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
local_llama
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Discussion: Biggest Roadblocks to Deploy LLMs to Production
I work with AWS daily, terraform, Python and java creating and maintaining enterprise solutions. I have played with sagemaker but it is so expensive I hate to leave it up for longer than a day. I downloaded and created a chat with your docs (entirely in airplane mode) here point being that I’ve hosted models both locally and in the cloud. But just ended up sticking to API calls as it’s so cheap
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You can now chat with your documents privately!
I posted the speed of mine in the readme https://github.com/jlonge4/local_llama
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Textgen webui for gpt_chatwithPDF
I would like to use this tool https://github.com/jlonge4/gpt_chatwithPDF/blob/main/gpt_chat_api.py but unfortunately the local version (https://github.com/jlonge4/local_llama) is bound to the CPU and thus quiet slow. Is there any way i could get textgenwebui working with the above stated tool?
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Is there a way to ask questions about ç multiple PDF files?
This is what you want https://github.com/jlonge4/local_llama it’s fully offline with no third parties, but the setup is a bit involved
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Newbie here. Need help with choosing a llm model for pdf ingestion and summarization locally
Or try this https://github.com/jlonge4/local_llama
- Local GPT (completely offline and no OpenAI!)
- Local GPT (completely offline and no OpenAI!) [P]
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Offline llama
Code here if interested
What are some alternatives?
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
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/
anything-llm - The all-in-one Desktop & Docker AI application with full RAG and AI Agent capabilities.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
gpt4all - gpt4all: run open-source LLMs anywhere
EmbedAI - An app to interact privately with your documents using the power of GPT, 100% privately, no data leaks
zep - Zep: Long-Term Memory for AI Assistants.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
chatdocs - Chat with your documents offline using AI.
langchain - 🦜🔗 Build context-aware reasoning applications
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.