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--- |
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library_name: keras-nlp |
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pipeline_tag: text-generation |
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--- |
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Gemma model fine-tuned on a comprehensive medical Q&A dataset to answer a variety of health-related questions, including drug usage, dosage, diseases, treatments, and side effects.This is a [`Gemma` model](https://keras.io/api/keras_nlp/models/gemma) uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends. |
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This model is related to a `CausalLM` task. |
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Model config: |
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* **name:** gemma_backbone |
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* **trainable:** True |
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* **vocabulary_size:** 256000 |
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* **num_layers:** 18 |
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* **num_query_heads:** 8 |
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* **num_key_value_heads:** 1 |
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* **hidden_dim:** 2048 |
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* **intermediate_dim:** 32768 |
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* **head_dim:** 256 |
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* **layer_norm_epsilon:** 1e-06 |
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* **dropout:** 0 |
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* **query_head_dim_normalize:** True |
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* **use_post_ffw_norm:** False |
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* **use_post_attention_norm:** False |
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* **final_logit_soft_cap:** None |
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* **attention_logit_soft_cap:** None |
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* **sliding_window_size:** 4096 |
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* **use_sliding_window_attention:** False |
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This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. |
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