Feature Extraction
Transformers
Safetensors
mists
time series
multimodal
TimeSeries-Text-to-Text
custom_code
Instructions to use HachiML/Mists-7B-v01-not-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HachiML/Mists-7B-v01-not-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HachiML/Mists-7B-v01-not-trained", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HachiML/Mists-7B-v01-not-trained", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_mists.py
Browse files- modeling_mists.py +5 -5
modeling_mists.py
CHANGED
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@@ -241,11 +241,11 @@ class MistsForConditionalGeneration(MistsPreTrainedModel):
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# モデルInput確認用(学習時)
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# print("model inputs:")
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print("input_ids: ", input_ids.shape if input_ids is not None else None)
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print("attention_mask: ", attention_mask.shape if attention_mask is not None else None)
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print("time_series_values: ", time_series_values.shape if time_series_values is not None else None)
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print("time_series_input_mask: ", time_series_input_mask.shape if time_series_input_mask is not None else None)
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print("==========")
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if inputs_embeds is None:
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# 1. Extra the input embeddings
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# モデルInput確認用(学習時)
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# print("model inputs:")
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+
# print("input_ids: ", input_ids.shape if input_ids is not None else None)
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| 245 |
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# print("attention_mask: ", attention_mask.shape if attention_mask is not None else None)
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| 246 |
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# print("time_series_values: ", time_series_values.shape if time_series_values is not None else None)
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| 247 |
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# print("time_series_input_mask: ", time_series_input_mask.shape if time_series_input_mask is not None else None)
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# print("==========")
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if inputs_embeds is None:
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# 1. Extra the input embeddings
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