AI Briefing#huggingface
The latest AI and developer news about #huggingface, with the original source and a short summary.
The latest AI and developer news about #huggingface, with the original source and a short summary.
Hugging Face has released a model card creator tool and guide for model documentation standards.
This guide explains how to efficiently explore, load, and process audio datasets using the Hugging Face `datasets` library.
Introduces Hugging Face's tools and guidance for identifying and mitigating bias throughout the entire machine learning development process.
The Bumblebee library has been released, enabling Hugging Face models such as GPT2 and Stable Diffusion to run in the Elixir environment.
This explains the concept of RLHF, which optimizes language models through human feedback, and its three-stage training process.
It explains a method for performing probabilistic time series forecasting that incorporates uncertainty by leveraging the Encoder-Decoder structure of the Transformer.
Reveals how to run Stable Diffusion on Apple Silicon via Core ML and releases the converted models.
This introduces the step-by-step model inference and deployment solutions offered by Hugging Face.
A feature has been integrated that allows machine learning demos to be run instantly from arXiv paper pages via Hugging Face Spaces.
HuggingFace is ending the paid tier of the Inference API and introducing a new pricing structure including Inference Endpoints.
Contrastive Search, which solves the text repetition problem, has been introduced to the Transformers library.
HuggingFace has released optimal settings for preventing overfitting and improving quality when training Dreambooth on Stable Diffusion using Diffusers.
This guide explains how to fine-tune the Whisper model on multilingual automatic speech recognition (ASR) datasets using Hugging Face Transformers.
Intel OpenVINO has been integrated into Hugging Face's Optimum Intel, making it easier to run model inference and quantization on Intel processors.
Hugging Face has added bias evaluation metrics to the 🤗 Evaluate library that can measure toxicity, polarity, and hurtfulness in language models.
MTEB, a large-scale text embedding benchmark supporting 8 tasks and 112 languages, has been released.
This introduces how to use Inference Endpoints, which allow easy and secure deployment of models from the Hugging Face Hub to managed infrastructure.
Hugging Face's Diffusers library now supports Flax, enabling fast inference of Stable Diffusion on TPU environments.
HuggingFace explains the technical process of optimizing the BLOOM model's inference server, improving latency by 5x and throughput by 50x.
Hugging Face has introduced DOI generation for permanent citation and reproducibility of models and datasets.
Hugging Face's no-code ML tool AutoTrain now supports image classification.
It explains how to leverage PyTorch's meta device to overcome memory constraints and efficiently load large language models.
SetFit, a few-shot learning framework that achieves high performance with little data and no prompts, has been released.
Hugging Face's Diffusers library has released version 0.3, adding key features such as Image-to-Image and Textual Inversion.