AGiXT
outlines
AGiXT | outlines | |
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26 | 33 | |
2,456 | 5,799 | |
- | 11.0% | |
9.9 | 9.7 | |
4 days ago | 3 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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AGiXT
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Conversational "memory loss"?
If you are more interested in AI assistants check out AGiXT. It has some really cool features but it is under heavy development. Not everything works jet and updates break sometimes already working functions. But it is still far better than babyAGI and other proof of concepts.
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
That's exactly my goal right now too! I have been trying to figure out how to use AGiXT agents to read and write to an "Adventurer's Log" text file to try to mimic a long term memory but honestly I'm not good enough with any of this to get it working yet. The idea I've got rn is that there'd be a DM agent which takes your input and then there'd be "memory" agents which would check text files such as "Adventurer's Log" and "Character Interactions/Relationships" to keep a contiguous understanding of what each character has done, who they've met, what they've been told/haven't been told by certain characters about their motivations. I'm sure there's someone *much* more talented than me working on this already, at this point I've sort of given up on the idea and I'm just waiting for someone to come out with a Tavern style interface where I can paste in world details and character details and just get going!
- AGiXT: A local automation platform with memories and SmartGPT-like prompting. Works with Ooba/LCPP/GPT4All, and more
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What are the best AI tools you've ACTUALLY used?
AGiXT: A Python package for AGI research.
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?Best LLM service for a tiny home server
Even if my (for example, privateGPT) LLM is glacially slow I'd still love to be able to say "Mr Holmes, have Mrs Doubtfire verb the data object in order to verb a product for me, please." (eg: analyse the wikipedia article on the peace of westfalia in order to ELI5 a short summary of it). Hopefully she'd crunch away at the data, and at my convenience, I could have her brief me on her conclusions. I'm sure folks here would do something more clever using AGiXT, or having the old girl prepare lesson-plans for Mycroft to deliver (I just think that sort of thing is world-changing-bonkers for anyone wanting to learn anything, perhaps for kids one day), but I'd have to work up to that.
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LlamaCPP and LangChain Agent Quality
Keep an eye on this project as well. https://github.com/Josh-XT/AGiXT
- Using the right prompt format makes responses so much better
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How big of a jump is 13B Vicuna Uncensored vs 30B Vicuna Uncensored?
File upload and automatic agents. It exists it is just buggy. They are working at an insane pace building it. It is practically broke 90% of the time. Maybe it's working better right now. I had success with v1.1.31 as well. https://github.com/Josh-xt/AGiXT
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Langchain, Langchain.js, vs AutoGPT for local agent development
Maybe you want to check out josh-xt/AGiXT it has its roots in langchain so you can see what the prompts look like and the code. They have made a lot of tools as well although you are going to have issues getting it to work. The newest version kinda works and version 1.1.31 I had the fast API backend working. Maybe you can help them out. They need more people to show them bugs. https://github.com/Josh-XT/AGiXT
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Is there an alternative to AgentGPT that I can run on my CPU with 32 GB of RAM?
https://github.com/Josh-XT/AGiXT I have tested this one and it is pretty much the same as AgentGPT, supports many providers + many local models (you can even make it work with oobabooga api which is pretty easy), don’t wait for insane results, the problem right now is context length with the local models, probably going to be an old issue in a few weeks we hope ;)
outlines
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Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens
> [2]: https://github.com/outlines-dev/outlines?tab=readme-ov-file#...
It's interesting as speech recognition has become more popular than ever through services like Alexa, and other iot devices support for OS speech recognition
Unfortunately most implementations (especially those that are iot focused) don't have very important features for robust speech recognition.
1. Ability to enable and disable a grammar
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Show HN: LLM-powered NPCs running on your hardware
[4] https://github.com/outlines-dev/outlines/tree/main
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Advanced RAG with guided generation
The next step is defining how to guide generation. For this step, we'll use the Outlines library. Outlines is a library for controlling how tokens are generated. It applies logic to enforce schemas, regular expressions and/or specific output formats such as JSON.
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
No benchmarks, just my anecdotal experience trying to get local LLM's to respond with JSON. The method above works for my use case nearly 100% of the time. Other things I've tried (e.g. `outlines`[0]) are really slow or don't work at all. Would love to hear what others have tried!
0 - https://github.com/outlines-dev/outlines
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Show HN: Chess-LLM, using constrained-generation to force LLMs to battle it out
As I was playing with the Outlines library (https://outlines-dev.github.io/outlines/), I discussed with my friend Maxime how funny it would be if we set up a way to pair LLMs in chess matches till one wins. The first time I tried it, it required substantial prompt engineering to get some of those LLMs to propose valid moves. Large language models can mostly stay focused and even play rather well; see https://news.ycombinator.com/item?id=37616170 for example. However small language models aren't as easy to convince.
Some of those LLMs have seen very little chess notation and so after the first few opening moves there aren't any valid tactics, let alone strategy, so they would end up either repeating the same move, or hallucinate moves that are not valid (Kxe5, but there would be a queen on e5!)
Then Outlines came along and we could force them to pick valid moves with little cost! Maxime worked super fast and got a first version of this idea as a gradio space.
I think it is pretty fun to see the (mostly terrible, but otherwise valid) chess that those LLMs play. Maybe it will even be instructive to how we can create small LLMs that can play much better than the ones on the leaderboard.
Anyway, you can check it out here:
https://huggingface.co/spaces/mlabonne/chessllm
What is interactive about it: you can pick the LLMs from available models on HuggingFace (within reason, small LLMs are preferable so that the space does not crash) or push one of your own small models to HF and have it fight with others. At the end of the game the leaderboard is updated.
Hope you find it fun!
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Show HN: Prompts as (WASM) Programs
> The most obvious usage of this is forcing a model to output valid JSON
Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?
0. https://github.com/outlines-dev/outlines
1. https://github.com/guidance-ai/guidance
2. https://github.com/sgl-project/sglang
- Show HN: Fructose, LLM calls as strongly typed functions
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Unlocking the frontend – a call for standardizing component APIs pt.2
And I think “just” Markdown doesn’t quite cut it for safe guidance. For example: directly generating content for your components. But I’m really excited about tooling like outlines appearing, with a greater focus on guided generation for structured data. Because this is often what we actually need!
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Ask HN: What are some actual use cases of AI Agents?
It's pretty easy to force a locally running model to always output valid JSON: when it gives you probabilities for the next tokens, discard all tokens that would result in invalid JSON at that point (basically reverse parsing), and then apply the usual techniques to pick the completion only from the remaining tokens. You can even validate against a JSON schema that way, so long as it is simple enough.
There are a bunch of libraries for this already, e.g.: https://github.com/outlines-dev/outlines
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Launch HN: AgentHub (YC W24) – A no-code automation platform
https://github.com/outlines-dev/outlines/blob/7fae436345e621... squares with my experience using LLMs for anything real
sequence = generator("Alice had 4 apples and Bob ate 2. Write an expression for Alice's apples:")
What are some alternatives?
AgentOoba - An autonomous AI agent extension for Oobabooga's web ui
guidance - A guidance language for controlling large language models.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
json-schema-spec - The JSON Schema specification
babyagi
Constrained-Text-Genera
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
torch-grammar
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
langroid - Harness LLMs with Multi-Agent Programming