👉 You can play with the Keras chatbot arenawhile you read. Click here to open it in a new tab. 👈 Table of contents   1. Introduction   2. The experiment   3. Keras chatbot arena tech: Spaces, Gradio, TPUs, JAX and Keras      3.1 Why TPUs?      3.2 Why JAX and Keras?      3.3    

We are excited to welcome Google’s all-new vision language models, PaliGemma 2, a new iteration of PaliGemma. Like its predecessor, PaliGemma 2 uses the same powerful SigLIP for vision, but it upgrades to the latest Gemma 2 for the text decoder part. PaliGemma 2 comes with new pre-trained (pt) models, in sizes of 3B, 10B, and 28B parameters. All of them support various input resolutions: 224×224, 448×448, and 896×896. These combinations provide a lot of flexibility for different use cases, […]

The Data is Better Together community releases yet another important dataset for open source development. Due to the lack of open preference datasets for text-to-image generation, we set out to release an Apache 2.0 licensed dataset for text-to-image generation. This dataset is focused on text-to-image preference pairs across common image generation categories, while mixing different model families and varying prompt complexities. TL;DR? All results can be found in this collection on the Hugging Face Hub and code for pre- and […]

We are excited to announce that popular open models from Hugging Face are now available on Amazon Bedrock in the new Bedrock Marketplace! AWS customers can now deploy 83 open models with Bedrock Marketplace to build their Generative AI applications. Under the hood, Bedrock Marketplace model endpoints are managed by Amazon Sagemaker Jumpstart. With Bedrock Marketplace, you can now combine the ease of use of SageMaker JumpStart with the fully managed infrastructure of Amazon Bedrock, including compatibility with high-level APIs […]

Today, we are thrilled to announce the launch of LeMaterial, an open-source collaborative project led by Entalpic and Hugging Face. LeMaterial aims to simplify and accelerate materials research, making it easier to train ML models, identify novel materials and explore chemical spaces. ⚛️🤗 As a first step, we are releasing a dataset called LeMat-Bulk, which unifies, cleans and standardizes the most prominent material datasets, including Materials Project, Alexandria and OQMD — giving rise to a single harmonized data format with […]

Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code. A short demo video What is synthetic data and why is it useful? Synthetic data is artificially generated information that mimics real-world data. It allows overcoming data limitations by expanding or enhancing […]

TL;DR: We benchmark 2 representative agentic AI workload components, text embedding and text generation, on two Google Cloud Compute Engine Xeon-based CPU instances, namely N2 and C4. The results consistently shows that C4 has 10x to 24x higher throughput over N2 in text embedding and 2.3x to 3.6x higher throughput over N2 in text generation. Taking price into consideration, C4’s hourly price is about 1.3x of N2, in this sense, C4 keeps 7x ~ 19x TCO(Total Cost of Ownership) advantage […]

We introduce Falcon3, a family of decoder-only large language models under 10 billion parameters, developed by Technology Innovation Institute (TII) in Abu Dhabi. By pushing the boundaries of performance and training efficiency, this release reflects our ongoing commitment to advancing open and accessible large foundation models. Falcon3 represents a natural evolution from previous releases,    

We introduce Bamba-9B, an inference-efficient Hybrid Mamba2 model trained by IBM, Princeton, CMU, and UIUC on completely open data. At inference time, the model demonstrates 2.5x throughput improvement and 2x latency speedup compared to standard transformers in vLLM. To foster community experimentation, the model is immediately available to use in transformers, vLLM, TRL, and llama.cpp. We also release tuning, training, and extended pretraining recipes with a stateful data loader, and invite the community to further improve this model. Let’s overcome […]

This blog post introduces ModernBERT, a family of state-of-the-art encoder-only models representing improvements over older generation encoders across the board, with a 8192 sequence length, better downstream performance and much faster processing. ModernBERT is available as a slot-in replacement for any BERT-like models, with both a base (149M params) and large (395M params) model size. Click to see how to use these models with transformers ModernBERT will be included in v4.48.0 of transformers. Until then, it requires installing transformers from […]