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.
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.
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)