update requirements and add float16 support

[SumanthRH]

What does this PR do?

I've been playing around with the library recently and noticed that some of the files were a bit outdated. This is a simple PR to

• update requirements
• cleanup a bit of the device casting and make it more automatic (still needs some work, but probably in a later PR)
• allow passing kwargs to the model during initialization. This will allow us to, for example, use bfloat16 dtype for memory efficiency. I made a small change in the LM.to() method for dtype casting.

Ex: lm = ecco.from_pretrained("distilgpt2", activations=True, torch_dtype=torch.bfloat16)

I've tested attributions and neuron factors notebooks with the new changes and things seem to work well

[SumanthRH]

Okay so I ran some more tests on using bfloat16 and it works for primary attributions, neuron factors, output token scores, token rankings and for visualizing the evolution of a token. Hoping to see this merged @jalammar !

[jalammar]

Thanks @SumanthRH! Merging this into a branch to update the CI/CD python versions.

Which version of python did you test this on?

[SumanthRH]

I tested this on python 3.10!