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.
- Low memory footprint(thanks quantization)
- Fast inference(thanks io binding)
Particularly in case of alpaca I have seen a 5x decrease in latency on A100 and 10x on AMD EPYC. I believe this is the way for users to have an AI that could genereate a response as fast as it can on their hardware. I have also added a link to my profile on hf with small alpacas turned into ONNX format. Take a look into them.
Or is this work "transferable" to other LLMs, once they become available?