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README.md
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4. **Position-specific variations**: In standard RoPE, frequency decreases with dimension index, but F0 adaptation modify this pattern.
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```python
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freqs = (theta / 220.0) * 700 * (torch.pow(10, torch.linspace(0, 2595 * torch.log10(torch.tensor(1 + 8000/700)), dim // 2, device=device, dtype=dtype) / 2595) - 1) / 1000
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```
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----
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The patterns below show how positions "see" each other in relation to theta and f0.
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4. **Position-specific variations**: In standard RoPE, frequency decreases with dimension index, but F0 adaptation modify this pattern.
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```python
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freqs = (theta / 220.0) * 700 * (torch.pow(10, torch.linspace(0, 2595 * torch.log10(torch.tensor(1 + 8000/700)), dim // 2, device=device, dtype=dtype) / 2595) - 1) / 1000
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```
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The patterns below show how positions "see" each other in relation to theta and f0.
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