Word on the street is the project has yielded largely unimpressive results compared to its potential, but NV is still investing in an attempt to further raise the GPU saturation waterline.
p.s. This project logo stood out to me at presenting the Llama releasing some "steam" with gusto. I wonder if that was intentional? Sorry for the immature take but stopping the scatological jokes is tough.
"LLaMA Factory is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA Factory, you can fine-tune hundreds of pre-trained models locally without writing any code."
hall0ween · 31m ago
are there any use cases, aside from code generation and formatting, where fine-tuning consistently useful?
clipclopflop · 49s ago
Creating small, specialized models for specific tasks. Being able to leverage the up front training/data as a generalized base allows you to quickly create a small local model that can generate outputs for that task that can come close to or match the same you would see in a large/hosted model.
https://www.nvidia.com/en-us/ai/nim-for-manufacturing/
Word on the street is the project has yielded largely unimpressive results compared to its potential, but NV is still investing in an attempt to further raise the GPU saturation waterline.
p.s. This project logo stood out to me at presenting the Llama releasing some "steam" with gusto. I wonder if that was intentional? Sorry for the immature take but stopping the scatological jokes is tough.
I found this link more useful.
"LLaMA Factory is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA Factory, you can fine-tune hundreds of pre-trained models locally without writing any code."