Provides unified model definitions and a single API for pretrained text, vision, audio, and multimodal models for both training and inference. Emphasizes cross-framework compatibility (PyTorch/TF/JAX), pipeline-based inference, and direct access to 1M+ Hub checkpoints.
Turns model definitions into a shared layer across training and inference stacks, covering text, vision, audio, video, and multimodal models. Pipelines, Trainer, and generation APIs make pretrained models usable without locking teams to one framework.
Pretrained uncased English BERT base model for masked language modeling and next-sentence prediction. ~110M parameters, pretrained on BookCorpus and English Wikipedia; commonly fine-tuned for classification, token labeling, and question answering.
Encodes sentences and short paragraphs into 384-dimensional semantic vectors for retrieval, clustering, and similarity tasks. Fine-tuned from MiniLM-L6-H384 on ~1.17B sentence pairs with a contrastive objective; defaults to 256 word-piece truncation.
Autoregressive transformer that generates English text from a prompt and can be fine-tuned for downstream NLP tasks. This is the 124M-parameter GPT-2 checkpoint (causal LM, byte-level BPE, 1024-token context) hosted on Hugging Face.
A compact pretrained bidirectional Transformer distilled from BERT base for masked language modeling and downstream NLP fine-tuning. Trained via knowledge distillation on BookCorpus and English Wikipedia, it reduces size/latency (≈67M params) while preserving BERT-like representations.
Transformer-based foundation model for tabular data that provides pre-trained checkpoints for fast classification and regression, with GPU-accelerated local inference and an optional cloud client. Best suited for small-to-medium datasets (~≤50k rows).
Runs pretrained diffusion models for image, video, and audio generation through composable pipelines. It separates pipelines, schedulers, models, adapters, and memory optimizations so teams can prototype quickly without locking into one model family.
Multilingual sequence-to-sequence speech model and toolkit for speech recognition, speech-to-text translation, and language identification. Offers several model sizes (tiny → large/turbo) for different speed/accuracy trade-offs and ships with a CLI and Python API for offline transcription workflows.
Rust-and-Python toolkit that serves open-source LLMs (Llama, Falcon, Mixtral, StarCoder) over HTTP/gRPC with tensor parallelism, continuous batching, Flash/Paged Attention and quantization. Now in maintenance mode, pointing users toward vLLM and SGLang.
Provides code and pretrained models for WeatherNext 2 and WeatherNext Cyclones — ML-based global, medium-range atmospheric forecasting models from Google DeepMind/Google Research. Includes Colab demos, pretrained weights, and guides for cloud access and inference.