MMMercy2

You can try the smaller 7B version.

You can use this command to apply the delta weights. (https://github.com/lm-sys/FastChat#vicuna-13b) The delta weights are hosted on huggingface and will be automatically downloaded.

They are the parameters of this large language model. There are 13B fp16 numbers.

There are certainly some effective language model benchmarks; however, they are not well-suited for evaluating a chat assistant. Some projects employ human evaluation, while this blog post explores an alternative approach based on GPT-4. Both methods have their advantages and disadvantages, making this blog post an intriguing case study that can inspire the future development of more comprehensive evaluations.

I am a Vicuna developer. We plan to release the weights once we have addressed all concerns and have a low-resource version of the inference code ready. We released the demo first to get some early feedback on the model.

This project fine-tunes LLaMA on ShareGPT and gets competitive performance compared to Google's Bard.

https://vicuna.lmsys.org/

It can do some basic coding. The code syntax highlighting looks cool.

> Do a quick sort in python

> Here's an example of a quicksort implementation in Python:

def quicksort(array): if len(array) <= 1: return array pivot = array[0] less = [x for x in array[1:] if x <= pivot] greater = [x for x in array[1:] if x > pivot] return quicksort(less) + [pivot] + quicksort(greater)