chatgpt-failures VS Open-Assistant

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

chatgpt-failures

Failure archive for ChatGPT and similar models (by giuven95)

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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chatgpt-failures Open-Assistant
20 329
574 36,667
- 0.4%
1.2 8.3
about 1 year ago 6 days ago
Python Python
- 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.

chatgpt-failures

Posts with mentions or reviews of chatgpt-failures. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-14.

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 chatgpt-failures and Open-Assistant you can also consider the following projects:

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

KoboldAI-Client

Milvus - A cloud-native vector database, storage for next generation AI applications

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

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

reflex - πŸ•ΈοΈ Web apps in pure Python 🐍

llama - Inference code for Llama models

gpt4all - gpt4all: run open-source LLMs anywhere

othello_world - Emergent world representations: Exploring a sequence model trained on a synthetic task