Add model weights

[ElArkk]

Small PR to add the pickled version of the 256 and 64 original model weights. This adds another 10 MB to the package, @ericmjl I think that is ok given that the 1900 weights that we already ship are around 70 MB in size anyways?

This doesn't change anything about the fact that 1900 weights will be loaded by default, but one can now use load_params to get the original 256 and 64 weights (those models being stacked mLSTMs makes it a bit of a pain to load up the weights manually from the .npy files)

[codecov]

Codecov Report

Merging #106 (91b114b) into master (4cbf902) will increase coverage by 0.38%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #106      +/-   ##
==========================================
+ Coverage   97.51%   97.90%   +0.38%     
==========================================
  Files          12       12              
  Lines         524      524              
==========================================
+ Hits          511      513       +2     
+ Misses         13       11       -2     

Flag	Coverage Δ
python	97.90% <100.00%> (?)

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Impacted Files	Coverage Δ
jax_unirep/utils.py	97.67% <100.00%> (ø)
jax_unirep/sampler.py	98.52% <0.00%> (+2.94%)	⬆️

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[ElArkk]

I just realised that we need to decide on whether we want to just add the weights to the repo, or also ship them with the package. If we go for the latter, we would need to adapt the API a bit to make the new 256 and 64 weights loadable, probably by a keyword arg in load_params?

[ericmjl]

@ElArkk, let's get back to this PR once you're done with your thesis work :). I'm going to leave it open

[ElArkk]

I think we can ship the 256 and 64 weights with the package as well, those 10 MB more don't make much of a difference on package size anymore. Do you agree @ericmjl ? I made them loadable thorugh a kwarg in load_params and added them to the package data.

I wanted to migrate all weight files to git LFS in this PR (I'm following this guide here https://notiz.dev/blog/migrate-git-repo-to-git-lfs). But doing so overwrites branch history, and I need to force push the migration. Better to do this in a seperate PR in case stuff breaks I think.

[ericmjl]

A few small changes suggested, @ElArkk. Would you also like to add in tests to make sure that the weights are loaded correctly and that we can do a forward pass of protein sequences through them? I think once that is done we can call this a reliably written model.

[ElArkk]

Yes tests are on the todo! If we'd want to also allow for users to easily rep protein sequences using the paper's 256 and 64 model we'd need to rewrite the get_reps function a bit since those two models are stacked lstm's (right now only single layer lstm's of variable size can be used with get_reps)

[ericmjl]

Merged!