[docs] Redesign (#31757)
* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
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# Machine learning apps
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[Gradio](https://www.gradio.app/), a fast and easy library for building and sharing machine learning apps, is integrated with [`Pipeline`] to quickly create a simple interface for inference.
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Before you begin, make sure Gradio is installed.
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```py
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!pip install gradio
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```
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Create a pipeline for your task, and then pass it to Gradio's [Interface.from_pipeline](https://www.gradio.app/docs/gradio/interface#interface-from_pipeline) function to create the interface. Gradio automatically determines the appropriate input and output components for a [`Pipeline`].
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Add [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to create a web server and start up the app.
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```py
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from transformers import pipeline
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import gradio as gr
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pipeline = pipeline("image-classification", model="google/vit-base-patch16-224")
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gr.Interface.from_pipeline(pipeline).launch()
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```
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The web app runs on a local server by default. To share the app with other users, set `share=True` in [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to generate a temporary public link. For a more permanent solution, host the app on Hugging Face [Spaces](https://hf.co/spaces).
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```py
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gr.Interface.from_pipeline(pipeline).launch(share=True)
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```
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The Space below is created with the code above and hosted on Spaces.
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<iframe
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src="https://stevhliu-gradio-pipeline-demo.hf.space"
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frameborder="0"
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width="850"
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height="850"
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></iframe>
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