gorilla
prompt-engineering
gorilla | prompt-engineering | |
---|---|---|
51 | 18 | |
10,118 | 7,988 | |
- | 2.3% | |
8.9 | 5.1 | |
4 days ago | 7 months ago | |
Python | ||
Apache License 2.0 | MIT License |
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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
prompt-engineering
- Ask HN: Any good collection of writing prompts for GPT 3.5/4?
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Show HN: LLM Agent Paper List
An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).
You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react
- FLaNK Stack Weekly for 20 June 2023
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What are your long-term career goals?
Well, if developers get replaced by AI, then who are the managers going to manage :). I personally don't think AI is just going to replace us. The way we work will continue to change as new AI tools come out. I'm taking time to tinker with new tools and seeing how others do as well (e.g., I found Brex's tips and tricks for working with LLMs very insightful: https://github.com/brexhq/prompt-engineering).
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A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.
These are good straight-to-the-point guides:
- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering
- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...
- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...
- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...
tl;dr:
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(2/2) May 2023
Brex's Prompt Engineering Guide (https://github.com/brexhq/prompt-engineering)
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
- Brex’s Prompt Engineering Guide
What are some alternatives?
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
Voyager - An Open-Ended Embodied Agent with Large Language Models
Prompt-Engineering-Guide - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
gorilla-cli - LLMs for your CLI
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
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.
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
chathub - All-in-one chatbot client
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
canal - 阿里巴巴 MySQL binlog 增量订阅&消费组件