gorilla
LocalAI
| gorilla | LocalAI | |
|---|---|---|
| 51 | 83 | |
| 10,118 | 19,862 | |
| - | 8.3% | |
| 8.9 | 9.9 | |
| 4 days ago | 5 days ago | |
| Python | C++ | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
LocalAI
- LocalAI: Self-hosted OpenAI alternative reaches 2.14.0
- Drop-In Replacement for ChatGPT API
- Voxos.ai – An Open-Source Desktop Voice Assistant
- Ask HN: Set Up Local LLM
- FLaNK Stack Weekly 11 Dec 2023
- Is there any open source app to load a model and expose API like OpenAI?
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What do you use to run your models?
If you're running this as a server, I would recommend LocalAI https://github.com/mudler/LocalAI
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OpenAI Switch Kit: Swap OpenAI with any open-source model
LocalAI can do that: https://github.com/mudler/LocalAI
https://localai.io/features/openai-functions/
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"ChatGPT romanesc"
De inspirație, LocalAI, un replacement la OpenAI. E deja hot pe GitHub.
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Local LLM's to run on old iMac / Hardware
Your hardware should be fine for inferencing, as long as you don't bother trying to get the GPU working.
My $0.02 would be to try getting LocalAI running on your machine with OpenCL/CLBlas acceleration for your CPU. If you're running other things, you could limit the inferencing process to 2 or 3 threads. That should get it working; I've been able to inference even 13b models on cheap Rockchip SOCs. Your CPU should be fine, even if it's a little outdated.
LocalAI: https://github.com/mudler/LocalAI
Some decent models to start with:
TinyLlama (extremely small/fast): https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-GGU...
Dolphin Mistral (larger size, better responses: https://huggingface.co/TheBloke/dolphin-2.1-mistral-7B-GGUF
What are some alternatives?
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
gpt4all - gpt4all: run open-source LLMs anywhere
Voyager - An Open-Ended Embodied Agent with Large Language Models
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
gorilla-cli - LLMs for your CLI
llama-cpp-python - Python bindings for llama.cpp
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.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.