Instructions to use ssdataanalysis/gemma-4-E4B-hebrew-first with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssdataanalysis/gemma-4-E4B-hebrew-first with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ssdataanalysis/gemma-4-E4B-hebrew-first", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fix: add assistant_only_loss=False to prevent all labels being masked to -100
Browse files
train.py
CHANGED
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@@ -122,6 +122,7 @@ training_args = SFTConfig(
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weight_decay=0.01,
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max_length=2048,
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packing=False,
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bf16=True,
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logging_strategy="steps",
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logging_steps=10,
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@@ -133,7 +134,7 @@ training_args = SFTConfig(
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push_to_hub=True,
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hub_model_id=output_dir,
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report_to="trackio",
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-
run_name=output_dir.replace("/", "-") + "-optimal
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remove_unused_columns=False,
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disable_tqdm=True,
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dataset_num_proc=4,
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weight_decay=0.01,
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max_length=2048,
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packing=False,
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+
assistant_only_loss=False, # CRITICAL FIX: prevent all labels being masked to -100
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| 126 |
bf16=True,
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logging_strategy="steps",
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logging_steps=10,
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push_to_hub=True,
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hub_model_id=output_dir,
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report_to="trackio",
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| 137 |
+
run_name=output_dir.replace("/", "-") + "-optimal",
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| 138 |
remove_unused_columns=False,
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| 139 |
disable_tqdm=True,
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dataset_num_proc=4,
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