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  We evaluated the xTrimoPGLM-CLM (xTCLM) and xTrimoPGLM(100B) models on two OOD test sets, one with sequence identity lower than 0.9 with the training set (<0.9 ID) and the other with sequence identity lower than 0.5 with the training set (<0.5 ID). Each OOD dataset comprises approximately 10,000 protein sequences. The perplexity results, compared against ProGen2-xlarge (6.4B), are as follows (lower is better):
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- | Model | ProGen2-xlarge (6.4B) | xTCLM (1B) | xTCLM (3B) | xTCLM (7B) | xT (100B) | xT (100B)-INT4 |
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- |:--------------------|:----------:|:----------:|:----------:|:--------------------:|:--------------------:|:--------------------:|
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- | < 0.9 ID | 9.7 | 9.8 | 9.3 | 8.9 | **8.7** | **8.9** |
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- | < 0.5 ID | 14.3 | 14.0 | 13.7 | 13.5 | **13.3** | **13.5** |
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  ## How to use
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  ```python
 
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  We evaluated the xTrimoPGLM-CLM (xTCLM) and xTrimoPGLM(100B) models on two OOD test sets, one with sequence identity lower than 0.9 with the training set (<0.9 ID) and the other with sequence identity lower than 0.5 with the training set (<0.5 ID). Each OOD dataset comprises approximately 10,000 protein sequences. The perplexity results, compared against ProGen2-xlarge (6.4B), are as follows (lower is better):
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+ | Model | ProGen2-xlarge (6.4B) | xTCLM (1B) | xTCLM (3B) | xTCLM (7B) | xT (100B)-INT4 |
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+ |:--------------------|:----------:|:----------:|:----------:|:--------------------:|:--------------------:|
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+ | < 0.9 ID | 9.7 | 9.8 | 9.3 | 8.9 | **8.9** |
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+ | < 0.5 ID | 14.3 | 14.0 | 13.7 | 13.5 | **13.5** |
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  ## How to use
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  ```python