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+ https://huggingface.co/HuggingFaceM4/tiny-random-LlamaForCausalLM
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+
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+ ```python
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+ from sparseml.transformers import SparseAutoModelForCausalLM, SparseAutoTokenizer, oneshot
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+ from sparseml.modifiers import SparseGPTModifier
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+
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+ model_id = "HuggingFaceM4/tiny-random-LlamaForCausalLM"
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+ compressed_model_id = "mgoin/tiny-random-LlamaForCausalLM-pruned95-compressed"
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+
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+ # Apply SparseGPT to the model
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+ oneshot(
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+ model=model_id,
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+ dataset="open_platypus",
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+ recipe=SparseGPTModifier(sparsity=0.95),
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+ output_dir="temp-output",
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+ )
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+
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+ model = SparseAutoModelForCausalLM.from_pretrained("temp-output", torch_dtype="auto")
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+ tokenizer = SparseAutoTokenizer.from_pretrained(model_id)
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+
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+ model.save_pretrained(compressed_model_id.split("/")[-1], save_compressed=True)
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+ tokenizer.save_pretrained(compressed_model_id.split("/")[-1])
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+
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+ # Upload the checkpoint to Hugging Face
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+ from huggingface_hub import HfApi
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+ HfApi().upload_folder(
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+ folder_path=compressed_model_id.split("/")[-1],
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+ repo_id=compressed_model_id,
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+ )
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+ ```