AutoGPTQ VS catai

Compare AutoGPTQ vs catai and see what are their differences.

AutoGPTQ

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm. (by AutoGPTQ)
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AutoGPTQ catai
19 7
3,806 408
5.0% 1.7%
9.3 8.6
5 days ago 3 months ago
Python TypeScript
MIT License MIT License
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.

AutoGPTQ

Posts with mentions or reviews of AutoGPTQ. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-10.

catai

Posts with mentions or reviews of catai. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-11.

What are some alternatives?

When comparing AutoGPTQ and catai you can also consider the following projects:

exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.

alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer

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

HyunGPT - chatbot thing

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

basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.

dalai - The simplest way to run LLaMA on your local machine

GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ

mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.

self-refine - LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.