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Create README.md

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+ ```python
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+ import sparseml.transformers
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+
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+ original_model_name = "Xenova/llama2.c-stories110M"
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+ output_directory = "output/"
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+ final_model_name = "nm-testing/llama2.c-stories110M-pruned2.4"
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+
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+ dataset = "open_platypus"
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+
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+ recipe = """
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+ test_stage:
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+ obcq_modifiers:
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+ SparseGPTModifier:
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+ sparsity: 0.5
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+ sequential_update: true
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+ quantize: false
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+ mask_structure: '2:4'
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+ targets: ['re:model.layers.\d*$']
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+ """
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+
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+ # Apply SparseGPT to the model
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+ sparseml.transformers.oneshot(
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+ model_name_or_path=original_model_name,
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+ dataset_name=dataset,
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+ recipe=recipe,
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+ output_dir=output_directory,
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+ )
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+
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+ # Upload the output model to Hugging Face Hub
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+ from huggingface_hub import HfApi
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+
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+ HfApi().upload_folder(
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+ folder_path=output_directory,
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+ repo_id=final_model_name,
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+ )
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+ ```