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Transformers

Model ● Covered in 18 stories Compare ⇄ + Follow

Transformers is a model architecture that uses attention mechanisms as described in the foundational "Attention is All You Need" paper and serves as a baseline for comparing alternative architectures like Mamba. The Hugging Face Transformers library has become a standardized model definition layer supporting over 300 model architectures with approximately 3 new architectures added weekly, integrating with training frameworks and inference engines to enable seamless interoperability across the ML ecosystem. Recently, the library has expanded to support computer vision models from timm, added constrained beam search for controlled text generation, integrated with quantization and inference optimization tools, and begun tracking carbon emissions from model training.

Updated 3 August 2026

Specifications

No specifications recorded yet.

Latest developments

Everything a Senior Engineer Needs to Know About What's Inside an LLM TLDR Dev · 1 month ago · 41

PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters Hugging Face Blog · 1 month ago · 34

Is it agentic enough? Benchmarking open models on your own tooling Hugging Face Blog · 1 month ago · 1

PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend Hugging Face Blog · 2 months ago · 45

The PR you would have opened yourself Hugging Face Blog · 3 months ago · 29

The Transformers Library: standardizing model definitions Hugging Face Blog · 1 year ago · 47

SmolVLM2: Bringing Video Understanding to Every Device Hugging Face Blog · 1 year ago · 18

Timm ❤️ Transformers: Use any timm model with transformers Hugging Face Blog · 1 year ago · 12

Timeline

Month Quarter Year

June 2026

May 2026

18 May PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

April 2026

16 Apr The PR you would have opened yourself

May 2025

15 May The Transformers Library: standardizing model definitions

February 2025

20 Feb SmolVLM2: Bringing Video Understanding to Every Device

January 2025

16 Jan Timm ❤️ Transformers: Use any timm model with transformers

March 2024

28 Mar Mamba Explained

December 2023

Mistral AI releases Mixtral 8x7B, an open-weights mixture-of-experts language model Model release

11 Dec Welcome Mixtral - a SOTA Mixture of Experts on Hugging Face

July 2022

25 Jul Deploying TensorFlow Vision Models in Hugging Face with TF Serving

May 2022

10 May Accelerated Inference with Optimum and Transformers Pipelines

April 2022

22 Apr CO2 Emissions and the 🤗 Hub: Leading the Charge
5 Apr ~Don't~ Repeat Yourself

March 2022

11 Mar Guiding Text Generation with Constrained Beam Search in 🤗 Transformers

February 2022

1 Feb Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers

January 2022

12 Jan Boosting Wav2Vec2 with n-grams in 🤗 Transformers

December 2019

5 Dec Deep double descent

Relationships

Products & technology

Hugging Face develops this model · 5 sources
Integrated with TensorFlow · 1 source
Integrated with Wav2Vec2 · 1 source
Integrated with pyctcdecode · 1 source
Integrated with timm · 1 source
Integrated with Axolotl · 1 source
Integrated with vLLM · 1 source
Integrated with llama.cpp · 1 source
Optimum integrated with this model · 1 source
Wav2Vec2 integrated with this model · 1 source
codecarbon integrated with this model · 1 source
PaddleOCR integrated with this model · 1 source

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TLDRocket 3 August 2026

The story of AI in August 2026 is one of consolidation meeting enforcement. Alibaba's Qwen3.8-Max, a 2.4-trillion-parameter mixture-of-experts model with a million-token context window, arrives as a direct challenge to OpenAI and Anthropic's frontier offerings—priced at $2 per million input tokens, it's positioning itself as the capable-and-cheaper alternative that China's AI push has long promised. But the more consequential development is structural: enterprises are finally getting the plumbing to actually deploy these models. June, backed by Salesforce's Marc Benioff, raised $20 million to automate AI integration into legacy systems, scanning corporate infrastructure and generating deployment workflows without expensive consultant fees. This solves what's become clear as the real bottleneck—not model capability, but implementation friction in messy, heterogeneous corporate environments.

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