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This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.

Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?

---------
Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>

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Upgrade all relevant versions and dependencies.

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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

One-line addition to the README to show how to run the service on a
machine without GPUs or CUDA support (e.g., for local prototyping)

---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

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Fixes #531

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See #626 
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---------
Co-authored-by: krzim <zimmerk4@live.com>

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As per title.


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![image](https://github.com/huggingface/text-generation-inference/assets/3841370/58177321-479f-4ad1-b3bc-cec027423984)

Closes #478

This PR starts the minimal possible amount of explanation I could think
of. It tries to explain how dynamic batching occurs, the interactions
with past key values and ignores the padding problem.

Maybe some drawings could help too but I kept it to text for now.

OPT models do not all have a `tokenizer.json` file on the hub at the
moment. Can't merge for now.