PatchTSMixer in HuggingFace
Hugging Face introduces PatchTSMixer, a lightweight MLP-Mixer-based model for multivariate time-series forecasting, now available in the Transformers library. The model is designed for efficient patch-based mixing of temporal and channel information. This integration expands Hugging Face's time-series modeling capabilities alongside the previously added PatchTST model.
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Patch Time Series Transformer in Hugging Face
Hugging Face has integrated PatchTST, a patch-based Transformer architecture for time series forecasting, into its ecosystem. PatchTST applies the patching concept from vision transformers to time series data, dividing sequences into subseries-level patches as input tokens. The blog post covers usage, fine-tuning, and zero-shot transfer capabilities of the model within the Hugging Face Transformers library.
Probabilistic Time Series Forecasting with Transformers
This Hugging Face blog post introduces probabilistic time series forecasting using Transformer-based models available in the Hugging Face ecosystem. It covers the application of attention-based architectures to sequential prediction tasks with uncertainty quantification. The post serves as a tutorial and capability demonstration for time series modeling within the Transformers library.
Welcome Mixtral - a SOTA Mixture of Experts on Hugging Face
Hugging Face published a blog post welcoming Mixtral, Mistral AI's Mixture of Experts (MoE) language model, to the platform. The post covers Mixtral's architecture, which uses 8 experts with 2 active per token, and its integration into the Hugging Face ecosystem including transformers support. Mixtral was positioned as a state-of-the-art open-weights model competitive with much larger dense models.
Multivariate Probabilistic Time Series Forecasting with Informer
A Hugging Face blog post introduces the Informer model for multivariate probabilistic time series forecasting. The post covers the architecture and usage of Informer, which uses a sparse attention mechanism (ProbSparse) to handle long sequences more efficiently than standard Transformers. It demonstrates how to use the model via the Hugging Face Transformers library for forecasting tasks.
Speech Synthesis, Recognition, and More With SpeechT5
This Hugging Face blog post introduces SpeechT5, a unified pre-trained model for speech synthesis, recognition, and related tasks. The post covers the model's architecture and capabilities, and explains how to use it via the Hugging Face Transformers library. SpeechT5 is a Microsoft Research model that uses a shared encoder-decoder framework across multiple speech tasks.
From GPT2 to Stable Diffusion: Hugging Face arrives to the Elixir community
Hugging Face announces Bumblebee, a library bringing Hugging Face model support to the Elixir programming language ecosystem. The integration enables Elixir developers to run models including GPT-2 and Stable Diffusion via the Nx numerical computing library. This expands the reach of Hugging Face's model hub beyond Python-centric workflows into the BEAM/Elixir ecosystem.
Timm ❤️ Transformers: Use any timm model with transformers
Hugging Face has announced native integration between the timm library and the Transformers library, allowing any timm vision model to be used directly within the Transformers ecosystem. This integration simplifies workflows for computer vision practitioners by enabling unified model loading, pipelines, and tooling across both libraries. The move consolidates Hugging Face's position as the central hub for model interoperability in the ML ecosystem.
Thinking Machines Lab Reveals TML-Interaction-Small: Real-Time Multimodal Interaction Model
Thinking Machines Lab (founded by Mira Murati) has announced TML-Interaction-Small, a 276B-parameter mixture-of-experts multimodal model that processes audio, video, and text concurrently using 200ms 'micro-turns' rather than waiting for conversational turns to complete. The architecture uses encoder-free early fusion, pairing a fast foreground interaction model with an asynchronous background reasoning model that shares context. On interactivity benchmarks (FD-bench V1/V1.5), it outperforms GPT-Realtime-2 and Gemini-3.1-flash-live-preview, though it trails GPT-Realtime-2 on intelligence benchmarks. A closed research preview is expected in coming months with wider release later in 2026.