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Description:
I’m working to integrate Uni-Mol2 into the unimol_tools repository and encountered some issues with configuration compatibility. I tried modifying the architecture and model configuration, as well as adjusting mol.dict to mol.dict_new.txt, but I’m not achieving the expected performance.
Here’s a summary of the integration steps I took:
Modified Architecture in unimol.py
Substituted molecule_architecture parameters to match Uni-Mol2 specifications.
Adjustments include reducing encoder_layers to 12, encoder_embed_dim to 768, and setting other parameters as per Uni-Mol2-84M specifications.
Model Configuration
Updated MODEL_CONFIG to point to the Uni-Mol2 checkpoint (checkpoint-84M.pt).
Adjusted path in weights and confirmed the correct .pt file location.
Dictionary Adjustment
Created a new dictionary file, mol.dict_new.txt, with 128 rows instead of the original 31, to match Uni-Mol2’s expected 128x128 dimension.
Observed Issue:
The model is not performing as anticipated, and the integration does not seem fully compatible with Uni-Mol2’s configuration. Specifically:
The expected behavior and accuracy are not achieved despite matching configurations to Uni-Mol2 specs.
Potential misalignment in the dictionary file or model configuration could be causing the issue.
Questions:
Could you identify any potential missteps or overlooked configuration requirements to ensure seamless integration?
Are there plans to add official support for Uni-Mol2 within the unimol_tools repository? This would be highly beneficial to ensure compatibility and streamline the setup process.
Steps to Reproduce:
Modify unimol.py as per the steps above.
Adjust MODEL_CONFIG to point to the checkpoint-84M.pt.
Replace mol.dict.txt with mol.dict_new.txt containing 128 rows.
Run the model and observe deviations from expected performance.
Thank you for your assistance and for considering future Uni-Mol2 support in unimol_tools!
The text was updated successfully, but these errors were encountered:
yes, unimol2 involves a different data preprocessing approach compared to unimol, and we plan to integrate unimol2 into unimol_tools in an upcoming version. however, I'm not certain whether this will be in a few weeks or months.
also welcome you to contribute this features.
yes, unimol2 involves a different data preprocessing approach compared to unimol, and we plan to integrate unimol2 into unimol_tools in an upcoming version. however, I'm not certain whether this will be in a few weeks or months. also welcome you to contribute this features.
would be amazing if unimol2 was integrated with unimol_tools
Description:
I’m working to integrate Uni-Mol2 into the
unimol_tools
repository and encountered some issues with configuration compatibility. I tried modifying the architecture and model configuration, as well as adjustingmol.dict
tomol.dict_new.txt
, but I’m not achieving the expected performance.Here’s a summary of the integration steps I took:
Modified Architecture in
unimol.py
molecule_architecture
parameters to match Uni-Mol2 specifications.encoder_layers
to 12,encoder_embed_dim
to 768, and setting other parameters as per Uni-Mol2-84M specifications.Model Configuration
MODEL_CONFIG
to point to the Uni-Mol2 checkpoint (checkpoint-84M.pt
).weights
and confirmed the correct.pt
file location.Dictionary Adjustment
mol.dict_new.txt
, with 128 rows instead of the original 31, to match Uni-Mol2’s expected128x128
dimension.Observed Issue:
The model is not performing as anticipated, and the integration does not seem fully compatible with Uni-Mol2’s configuration. Specifically:
Questions:
unimol_tools
repository? This would be highly beneficial to ensure compatibility and streamline the setup process.Steps to Reproduce:
unimol.py
as per the steps above.MODEL_CONFIG
to point to thecheckpoint-84M.pt
.mol.dict.txt
withmol.dict_new.txt
containing 128 rows.Thank you for your assistance and for considering future Uni-Mol2 support in
unimol_tools
!The text was updated successfully, but these errors were encountered: