Add gguf support for bloom

[VladOS95-cyber]

What does this PR do?

Add Bloom GGUF loading support

Fixes # (issue)

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• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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  to it if that's the case. Link: Community contribution: Adding GGUF support for more architectures #33260
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• Did you write any new necessary tests?

Who can review?

Regarding the task @SunMarc @LysandreJik @ArthurZucker .

[VladOS95-cyber]

Hi @SunMarc @LysandreJik @ArthurZucker! This PR is ready for review. There is one thing that looks odd to me. After dequantization and loading the model, It genereates a wrong sequence, not as expected when using a normal pretrained model. Instead oftensor([[59414, 15, 473, 3370, 4026, 427, 5894, 861, 473, 912, 5636]]) , it generates smth like [[59414, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]]. I cannot find a root cause of this problem, i've already checked mapping and so on several times, it should be correct. It looks like that weights are not correct but I am not sure...

[SunMarc]

This PR is ready for review. There is one thing that looks odd to me. After dequantization and loading the model, It genereates a wrong sequence, not as expected when using a normal pretrained model. Instead oftensor([[59414, 15, 473, 3370, 4026, 427, 5894, 861, 473, 912, 5636]]) , it generates smth like [[59414, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]]. I cannot find a root cause of this problem, i've already checked mapping and so on several times, it should be correct. It looks like that weights are not correct but I am not sure...

Since the model was quantized, THis is normal that it is not behaving the same of the normal pretrained model. Dequantization doesn't recover the precision of the original model. Could you check that it behaves similarly as the original model that was converted to gguf in fp16 precision or full precision ? This way we have a way to compare the model loaded from gguf file.