For Mistral 7B, which was trained at BF16, that works out to about 14 GB ... such as Unsloth and Hugging Face's Transformers Trainer. However, for this hands on, we're going to be using Axolotl.

However, if the model currently exceeds your GPU memory, you can try to pass the --precision bf16-true option ... For example, we can convert a LitGPT model to the Hugging Face format and save it via ...

Hugging Face co-founder and chief science officer Thomas Wolf thinks that AI today isn't capable of figuring out novel ...

Training LLMs on GPU Clusters, an open-source guide that provides a detailed exploration of the methodologies and ...

But if you want to fully control the large language model experience, the best way is to integrate Python and Hugging Face APIs together. The files Python requires to run your LLM locally can be found ...

Hugging Face's new FastRTC library enables Python developers to build real-time voice and video AI applications in just a few lines of code.

The chief science officer and cofounder of Hugging Face, an open-source AI company backed by Amazon and Nvidia, analyzed the limits of large language models in a Thursday post on X. He wrote that ...

But Thomas Wolf, Hugging Face's co-founder and chief science officer, has a more measured take. In an essay published to X on Thursday, Wolf said that he feared AI becoming "yes-men on servers ...