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chinsengi
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Feb 12, 2026
| unmasking_prob[batch], dtype=torch.float32 | ||
| ).unsqueeze(0) | ||
| EPS = 1e-6 | ||
| clamped_prob = torch.clamp(unmasking_prob[batch], min=EPS, max=1.0 - EPS) |
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I would advise against this clamping operation, since it will make likelihood estimation inaccurate.
| # If the model does not accept loss kwargs, we need to normalize the loss by the number of gradient accumulation steps | ||
| loss = loss / self.current_gradient_accumulation_steps | ||
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| # Turning off loss scaling w.r.t. gradient accumulation when DeepSpeed is enabled |
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WayneDW
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The code runs locally. Distributed setups require significant hyperparameter changes. Tuning is ongoing.