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README.md
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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## Model Details
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Tiny `Gemma2ForCausalLM` with randomly-initialized weights for testing. Code to generate:
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```py
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import torch
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from transformers import Gemma2ForCausalLM, Gemma2Config, AutoTokenizer
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# Set seed for reproducibility
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torch.manual_seed(0)
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# Initializing a Gemma2 gemma2-9b style configuration
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configuration = Gemma2Config(
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head_dim=16,
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hidden_size=32,
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initializer_range=0.02,
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intermediate_size=64,
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max_position_embeddings=8192,
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model_type="gemma2",
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num_attention_heads=2,
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num_hidden_layers=1,
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num_key_value_heads=2,
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vocab_size=256000,
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attn_implementation='eager',
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)
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# Initializing a model from the gemma2-9b style configuration
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model = Gemma2ForCausalLM(configuration)
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# Re-use gemma2 tokenizer
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tokenizer = AutoTokenizer.from_pretrained("Xenova/gemma2-tokenizer")
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# Upload to the HF Hub
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model_id = 'hf-internal-testing/tiny-random-Gemma2ForCausalLM'
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model.push_to_hub(model_id)
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tokenizer.push_to_hub(model_id)
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```
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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