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.
Introduces the concept and training method of Decision Transformer, which solves reinforcement learning as sequence modeling.
It proposes the OpenRAIL license, which addresses the limitations of existing open-source licenses to govern the responsible use of AI models.
It explains an 8-bit quantization technique that cuts memory usage of large language models by 2x while preserving performance, and its integration into the Hugging Face library.
Hugging Face has released the Skops library, which supports hub hosting and documentation of scikit-learn models.
Hugging Face has launched Enterprise Hub, integrating enterprise-grade security and collaboration tools.
New documentation and features for processing audio and image data have been added to the Hugging Face Datasets library.
By combining TensorFlow and XLA in Hugging Face's `transformers`, text generation speed can be increased by up to 100x.
This explains how to deploy Hugging Face's TensorFlow vision models using TF Serving.
This details the hardware and software technology used to train the 176B-parameter BLOOM model.
BLOOM, the world's largest open-source multilingual language model with 176 billion parameters, has been released.
It introduces how to apply DeepSpeed ZeRO technology through Hugging Face's Accelerate library to solve memory issues in large-scale model training.
Explains how to convert Transformers models to ONNX format and optimize inference performance using Hugging Face Optimum.
Intel and Hugging Face have partnered to release the Optimum Intel library, which supports hardware acceleration for Transformer models.
A variety of Transformer models optimized for Graphcore IPUs have been added to the Hugging Face Optimum library.
Pull Request and Discussion features have been added to the Hugging Face Hub for models, datasets, and Spaces.
Introducing TAPEX, which efficiently trains table tasks using synthetic data derived from SQL execution results.
Gradio has released version 3.0, which includes a frontend redesign and the Blocks API for custom layouts.
Hugging Face has updated the features of the Optimum library, which optimizes inference performance for Transformers models.
Hugging Face has raised a $100 million Series C round led by Lux Capital.
This introduces how to accelerate large-scale model training by leveraging PyTorch FSDP through Hugging Face's Accelerate library.
Hugging Face has introduced a feature to track and search CO2 emissions generated during model training and deployment.
The integration of Habana Labs' SynapseAI and Hugging Face's Optimum library accelerates Transformer training based on Gaudi processors.
It explains why Hugging Face adopted a 'single model file' policy instead of the DRY principle when designing the Transformers library.
Hugging Face has integrated Decision Transformer, an offline reinforcement learning model, into the transformers library.