Add options to load CLIP Transformer models
Author: amitkumarj441
Pull request: yes


# What does this PR do?

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Flexible options around loading CLIP transformer models

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## Who can review?

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Comments:
> Hey! Could you share the motivations behind this? 🤗

@ArthurZucker I needed to have an independent feature extractor using CLIP text model for generating (query) embeddings, though, [CLIPTextModelWithProjection](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/clip/modeling_clip.py#L1181C7-L1181C34) and similarly for `CLIP_vision` model has `AutoModelFor...` instance. So, to standardise this process around Transformer, we need options to avail these CLIP text/image models separately.

This is a custom usage and would recommend you to build on top of transformers. It's not really standard as you are gonna have missing keys and unexpected keys for both. 

> This is a custom usage and would recommend you to build on top of transformers. It's not really standard as you are gonna have missing keys and unexpected keys for both.

Yes, it is, though, the tweaking options to adopt independent CLIP models (text/image) should lie within `modeling_clip.py` for flexibility.

Sorry but no, if you only want the vision model or only want the text model you should use the CLIPVisionModel and the CLIPTextModel rather than add this 😉 

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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