petals VS Open-Assistant

Compare petals vs Open-Assistant and see what are their differences.

Open-Assistant

OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. (by LAION-AI)
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petals Open-Assistant
98 329
8,684 36,647
1.2% 0.3%
8.3 8.3
2 days ago 6 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

petals

Posts with mentions or reviews of petals. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-26.

Open-Assistant

Posts with mentions or reviews of Open-Assistant. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-08.

What are some alternatives?

When comparing petals and Open-Assistant you can also consider the following projects:

text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

KoboldAI-Client

llama - Inference code for Llama models

alpaca-lora - Instruct-tune LLaMA on consumer hardware

llama.cpp - LLM inference in C/C++

GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)

Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]

gpt4all - gpt4all: run open-source LLMs anywhere

nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.

stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.