generative_agents
outlines
generative_agents | outlines | |
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6 | 33 | |
15,486 | 6,311 | |
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5.7 | 9.7 | |
6 days ago | 3 days ago | |
Python | ||
Apache License 2.0 | Apache License 2.0 |
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generative_agents
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Show HN: LLM-powered NPCs running on your hardware
[2] https://github.com/joonspk-research/generative_agents/tree/m...
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Role-playing with AI will be a powerful tool for writers and educators
This is a cool project that implements role-playing AI:
https://github.com/joonspk-research/generative_agents
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Show HN: AI-town, run your own custom AI world SIM with JavaScript
If you haven't yet checked out the Generative Agents project referenced by OP, definitely give it a look, it's open source: https://github.com/joonspk-research/generative_agents
Over the weekend Lance Martin got it working with local models using llama.cpp and ollama.ai which saves $ on longer sims since all inference happens locally https://twitter.com/RLanceMartin/status/1690829179615657985. It's neat how the AI agents interface with each other – e.g. one will host a party and invites will be sent throughout the group
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Generative Agents: Interactive Simulacra of Human Behavior, Now Open Source
Side note -- discussion on adding support for local models is here, along with a preliminary fork that adds support: https://github.com/joonspk-research/generative_agents/issues...
- Generative agents: LLM-driven interactive simulation “inspired by The Sims”
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?
ai-town - A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.
guidance - A guidance language for controlling large language models.
cat-town - A custom AI-town with cats. Based on https://github.com/a16z-infra/AI-town
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
json-schema-spec - The JSON Schema specification
Constrained-Text-Genera
torch-grammar
langroid - Harness LLMs with Multi-Agent Programming
TypeChat - TypeChat is a library that makes it easy to build natural language interfaces using types.
llama.cpp - LLM inference in C/C++
BrainChulo - Harnessing the Memory Power of the Camelids
clownfish - Constrained Decoding for LLMs against JSON Schema