Time Series Forecasting
Transformers
Safetensors
sundial
text-generation
time series
time-series
forecasting
foundation models
pretrained models
generative models
time series foundation models
custom_code
Instructions to use thuml/sundial-base-128m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thuml/sundial-base-128m with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("thuml/sundial-base-128m", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update flow_loss.py
Browse files- flow_loss.py +1 -1
flow_loss.py
CHANGED
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@@ -39,7 +39,7 @@ class FlowLoss(nn.Module):
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def sample(self, z, num_samples=1):
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z = z.repeat(num_samples, 1)
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-
noise = torch.randn(z.shape[0], self.in_channels).
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x = noise
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dt = 1.0 / self.num_sampling_steps
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for i in range(self.num_sampling_steps):
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def sample(self, z, num_samples=1):
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z = z.repeat(num_samples, 1)
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+
noise = torch.randn(z.shape[0], self.in_channels).to(z.device)
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x = noise
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dt = 1.0 / self.num_sampling_steps
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for i in range(self.num_sampling_steps):
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