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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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This is
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- **Developed by:** [email protected]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** Korean/English
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This model card corresponds to the 2B base version of the Gemma model.
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## Model Details
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This is a model that separates terms into words and describes each separated word.
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- **Developed by:** [email protected]
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- **Language(s) (NLP):** Korean/English
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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vocab_size=len(tokenizer),
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torch_dtype = torch.float16,
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use_cache=False,
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#attn_implementation="flash_attention_2",
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device_map="auto")
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### Direct Use
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