open-llms
private-gpt
open-llms | private-gpt | |
---|---|---|
22 | 131 | |
10,168 | 51,882 | |
- | 2.3% | |
7.7 | 9.2 | |
about 1 month ago | about 22 hours ago | |
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.
open-llms
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7 SAAS ideas đź’ˇ you can steal
Everyone knows about ChatGPT by now, but did you know there are other models like "Mistral" or "Falcon" - you can view a full list of open-source models here or on huggingface.
- eugeneyan/open-llms
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GPT-4 API general availability
This is the most well-maintained list of commercially usable open LLMs: https://github.com/eugeneyan/open-llms
MPT, OpenLLaMA, and Falcon are probably the most generally useful.
For code, Replit Code (specifically replit-code-instruct-glaive) and StarCoder (WizardCoder-15B) are the current top open models and both can be used commercially.
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Local LLMs: After Novelty Wanes
There's also MPT, which has a 7B, and Falcon, with a 7B and 40B although they have not had the inference tuning in community projects that the llamas have had. This is a good repo for reviewing what's available atm: https://github.com/eugeneyan/open-llms
- How to keep track of all the LLMs out there?
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How do I learn AI/Machine Learning?
If I was going to do the same I would at least build off of something, check out https://github.com/eugeneyan/open-llms, you should at least have a decent understanding of artificial neural networks (ANNs) and this link is pretty good on the basic concepts you need inc classification and learning types, good luck friend.
- LLM and privacy
- Local LLM to learn, explore and use for commercial purpose
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Best instruct model recommendations to use with T4?
This list might help: https://github.com/eugeneyan/open-llms
- [D] What is the best open source LLM so far?
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!
What are some alternatives?
SillyTavern - LLM Frontend for Power Users.
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
SillyTavern-Extras - Extensions API for SillyTavern.
gpt4all - gpt4all: run open-source LLMs anywhere
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
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/
llm-jeopardy - Automated prompting and scoring framework to evaluate LLMs using updated human knowledge prompts
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
azure-search-openai-demo - A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
panml - PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation.
llama.cpp - LLM inference in C/C++