gorilla
outlines
gorilla | outlines | |
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51 | 32 | |
10,118 | 5,799 | |
- | 11.0% | |
8.9 | 9.7 | |
4 days ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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gorilla
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Launch HN: Nango (YC W23) – Open-Source Unified API
Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.
- Autonomous LLM agents with human-out-of-loop
- Show HN: I made a script to scrape your Facebook group
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Pushing ChatGPT's Structured Data Support to Its Limits
* Gorilla [https://github.com/ShishirPatil/gorilla]
Could be interesting to try some of these exercises with these models.
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Guidance for selecting a function-calling library?
gorilla
- Gorilla: An API Store for LLMs
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Show HN: OpenAPI DevTools – Chrome ext. that generates an API spec as you browse
Nice this made me go back and check up on the Gorilla LLM project [1] to see whats they are doing with API and if they have applied their fine tuning to any of the newer foundation models but looks like things have slowed down since they launched (?) or maybe development is happening elsewhere on some invisible discord channel but I hope the intersection of API calling and LLM as a logic processing function keep getting focus it's an important direction for interop across the web.
[1] https://github.com/ShishirPatil/gorilla
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RestGPT
"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ :
> Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them!
eval/:
- Calling APIs with Natural Language
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Shishir Patil: Teaching AI to Use APIs with Gorilla LLM – Humans of AI Podcast
Humans of AI Podcast #7
An amazing conversation with Shishir Patil the creator of the Gorilla LLM, a large language model specifically trained to use APIs!
Shishir is currently a 5th year PhD student at the University of California, Berkeley whose work broadly covers ML-Systems, LLMs, Edge-ML, and Sky computing.
Definitely give the episode a listen to hear Shishir's story.
And to read more about #GorillaLLM, check out the project page!
https://gorilla.cs.berkeley.edu
outlines
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Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens
It's coming! CMUSphinx used to have something like this, and there are some [1] solutions [2] on the horizon.
[1]: https://github.com/alphacep/vosk-api/issues/55
[2]: https://github.com/outlines-dev/outlines?tab=readme-ov-file#...
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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?
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
guidance - A guidance language for controlling large language models.
Voyager - An Open-Ended Embodied Agent with Large Language Models
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
gorilla-cli - LLMs for your CLI
json-schema-spec - The JSON Schema specification
Gin - Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
torch-grammar
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
Constrained-Text-Genera
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
langroid - Harness LLMs with Multi-Agent Programming