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README.md
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@ -59,12 +59,22 @@ limitations under the License.
</h3>
<h3 align="center">
<a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
</h3>
Transformers is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities.
There are over 500K+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer
vision, audio, video, and multimodal model, for both inference and training.
It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the
pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
simple, customizable, and efficient.
There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.

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docs/source/en/index.md
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# Transformers
Transformers is a library of pretrained natural language processing, computer vision, audio, and multimodal models for inference and training. Use Transformers to train models on your data, build inference applications, and generate text with large language models.
<h3 align="center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
</h3>
Explore the [Hugging Face Hub](https://huggingface.com) today to find a model and use Transformers to help you get started right away.
Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer
vision, audio, video, and multimodal model, for both inference and training.
It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the
pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
simple, customizable, and efficient.
There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
## Features