PrivateGPT4Linux
Voyager
PrivateGPT4Linux | Voyager | |
---|---|---|
23 | 53 | |
15 | 5,170 | |
- | 1.8% | |
4.1 | 4.7 | |
8 days ago | about 1 month ago | |
Shell | JavaScript | |
GNU General Public License v3.0 only | MIT License |
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PrivateGPT4Linux
- PrivateGPT: Interact with your documents using the power of GPT, 100% privately, no data leaks
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Need guidance in this sea of information on how to set up a local AI
I found things like this dataset and LocalAI and I followed the article to get PrivateGPT and the GPT4ALL groovy.bin but I'm completely lost and it feels like the more I research the internet or ask BingAI for answers, the more questions I get instead. At this stage I don't know what goes where, if there's a difference between source documents and datasets, should I run this from my 2tb SSD? Should I have the data on my 8tb HDD? Will all this even work on my PC?
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Several newb questions
No, as the same as the last question, It does not have access to anything except the model data itself. However, there are some approaches that can let LLMs have access LOCAL documents, which means if you can have a program that extracts data from the database into a local folder which contains TEXT files. This could also work for 2(I didn't mention it in 2 because online datas are REALLY big. It would take the model hours to give an answer. If the database is not large then there might be a shot. Check https://github.com/imartinez/privateGPT(Must be GPT4all compatible models sadly).
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What solution would best suite a SaaS - for reading and answering data from PDF files uploaded by users
I've been doing exactly this with an open source repository called PrivateGPT imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks (github.com)
- How to run an open source AI model, offline, on my own computer?
- Check out my script which installs privateGPT for Linux!
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are there anytools or frameworks similar to "langchain" or "llamaindexbut implemented or designed in a language other than python?
Not really, you will probably need to change the data location and the LLM provider in the example code to get it running. But you don't have to implement that yourself there are a couple projects that already do that like privateGPT. I use it for searching datasheets, got it up an running in a few hours and I'm pretty happy with it so far.
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Intern tasked to make a "local" version of chatGPT for my work
PrivateGPT can do that.
- I've made privateGPT work for Linux check it out (documents)
- I've made privateGPT work for Linux check it out
Voyager
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Google Launches Gemini, Its "Most Powerful" AI Model to Date
Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.
- Is there any game that allow us to interact with it by python?
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A Coder Considers the Waning Days of the Craft
> AI cannot sustain itself trained on AI work.
This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]
> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.
You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].
> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.
Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.
I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.
[0]: https://arxiv.org/abs/2309.05463
[1]: https://voyager.minedojo.org/
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AutoGen: Enable Next-Gen Large Language Model Applications
In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.
I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.
But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.
And yes I think the idea is that emergent behaviour can be useful. This comes to mind
https://github.com/MineDojo/Voyager
But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.
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Open/Local LLM support for MineDojo/Voyager
This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.
- Voyager – Minecraft Embodied Agent with Large Language Models
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List of Awesome AI Agents like AutoGPT and BabyAGI / Many open-source Agents with code included!
In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376
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[D] - Are there any AI benchmarks that involve successful longterm problem solving when running as autonomous agents (like in autogpt)? How do we compare the effectiveness of models as agents?
Does this beat the voyager? I read about it and wondered what if we add a skill library to langchain/llamaindex agents. It could be the same vector store for storing static data but after each task is performed, the agent will evaluate and archive the recipe of steps to perform a new task. Next time when the agent is asked to perform a task, it can just look at the library to retrieve a recipe. Unlike traditional fine tuning, you dont update the model parameters, these recipes are much more interpretable and can be manually edited/inserted by humans. There may also be an automatic way to convert wikihow articles or youtube tutorials into recipes.
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GPT-4 was set free in Minecraft, here's what happened next...
Source. P.S. If you love geeking over AI updates, I have this free newsletter you might want to check out. Thank you!
Source.
What are some alternatives?
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
GITM - Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
mineflayer - Create Minecraft bots with a powerful, stable, and high level JavaScript API.
llm - An ecosystem of Rust libraries for working with large language models
llm-awq - AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
llm-chain - `llm-chain` is a powerful rust crate for building chains in large language models allowing you to summarise text and complete complex tasks
gorilla - Gorilla: An API store for LLMs
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ