Hugging Face Introduces “T0”, An Encoder-Decoder Model That Consumes Textual Inputs And Produces Target Responses By Tanushree Shenwai - ...

To work with a dataset from Hugging Face and train a model with a classification layer using an encoder-only model, followed by a decoder model, we will follow the steps below. For this example, we ...

encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained` ...

Hugging Face Inc. today open-sourced SmolVLM-256M, a new vision language model with the lowest parameter count in its category. The algorithm’s small footprint allows it to run on devices such ...

Hugging Face has released SmolLM3, a 3B parameter language model that offers long-context reasoning, multilingual capabilities, and dual-mode inference, making it one of the most competitive ...

At a Glance Hugging Face introduced a new AI model called aMUSEd that can generate images within seconds. It uses a Masked Image Model architecture rather than latent diffusion, which reduces ...

Following the same encoder-decoder model architecture as BART, PEGASUS employs two self-supervised objectives for pre-training: Masked Language Modeling (MLM) and Gap Sentence Generation (GSG) for ...

Hugging Face has an improved vision language model. Smaller in size, Idefics2 features better image manipulation and character recognition.