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.
Explains how to implement graph classification using the Transformers library and the Graphormer model.
Snorkel AI is expanding its enterprise foundation model development environment by leveraging Hugging Face's Inference Endpoint.
Hugging Face released the StackLLaMA model and a guide for training LLaMA using RLHF.
Hugging Face is introducing new ethics tags and safeguards to support the ethical use of AI models.
A technical tutorial on implementing federated learning for language models by combining Hugging Face with the Flower framework.
Hugging Face Hub has introduced a feature that lets users view Jupyter Notebooks rendered directly on the platform.
Hugging Face has integrated TRL and PEFT to enable RLHF training of 20B-scale LLMs even on consumer GPUs.
Hugging Face's Diffusers library now supports ControlNet, a feature that enables precise control over Stable Diffusion's image generation process.
This covers a case where machine learning was used during the Turkey earthquake to structure rescue request information from social media and support rescue activities.
Hugging Face has announced an ethical framework for the responsible development of the Diffusers library.
Witty Works implemented a technical solution using SetFit to classify non-inclusive language based on context with only a small amount of data.
Hugging Face has released a native app that runs Stable Diffusion quickly on Mac by leveraging Core ML.
This covers the concepts and methodology of red-teaming to prevent harmful outputs from LLMs.
Fetch built its own AI models using Hugging Face and AWS to improve receipt processing efficiency and shorten development time.
Hugging Face and AWS are expanding their strategic partnership to accelerate the development and deployment of next-generation AI models.
Hugging Face revealed the performance and cost benefits gained from migrating its internal model serving from AWS ECS to Inference Endpoints.
The 🤗 PEFT library, which enables fine-tuning of large models with fewer resources, has been released.
SpeechT5, a unified speech processing model, has been added to the Hugging Face Transformers library.
Hugging Face has launched a multi-agent competition system that evaluates the performance of reinforcement learning agents using ELO ratings.
This explains the core training strategies and architecture of Vision-Language Models, which combine visual and language information.
Hugging Face is expanding its computer vision ecosystem, supporting 8 core vision tasks and over 3,000 models.
It introduces a method of applying LoRA technology to Stable Diffusion to speed up training and dramatically reduce model size.
By integrating Hugging Face's Optimum library with ONNX Runtime, training speed for transformer models has been improved by up to 130%.
The Hugging Face Hub now supports models from PaddlePaddle, Baidu's open-source deep learning framework.