Featured Models
Curated model picks indexed for search and discovery.
- Featured Modelsentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Featured Modellpiccinelli/unidepth-v2-vitl14
This model has been pushed to the Hub using the PytorchModelHubMixin integration:
- Featured Modelamazon/chronos-2
Update Jun 5, 2026: ☁️ Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the new deployment guide.
- Featured Modeltimm/mobilenetv3_small_100.lamb_in1k
Model card for mobilenetv3 small 100.lamb in1k
- Featured ModelBAAI/bge-reranker-v2-m3
More details please refer to our Github: FlagEmbedding.
- Featured Modelfacebook/opt-125m
OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.
- Featured ModelQwen/Qwen3-8B
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- Featured Modeltrl-internal-testing/tiny-Qwen2ForCausalLM-2.5
This is a minimal model built for unit tests in the TRL library.
- Featured Modelintfloat/multilingual-e5-small
Multilingual E5 Text Embeddings: A Technical Report.
- Featured Modelnomic-ai/nomic-embed-text-v1.5
nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning
- Featured Modelopenai-community/gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
- Featured Modelautogluon/chronos-bolt-small
🚀 Update Feb 14, 2025 : Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code.