---
library_name: keras-hub
pipeline_tag: text-generation
---
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.
This model is related to a `CausalLM` task.

Model config:
* **name:** gemma_backbone
* **trainable:** True
* **vocabulary_size:** 256000
* **num_layers:** 18
* **num_query_heads:** 8
* **num_key_value_heads:** 1
* **hidden_dim:** 2048
* **intermediate_dim:** 32768
* **head_dim:** 256
* **layer_norm_epsilon:** 1e-06
* **dropout:** 0
* **query_head_dim_normalize:** True
* **use_post_ffw_norm:** False
* **use_post_attention_norm:** False
* **final_logit_soft_cap:** None
* **attention_logit_soft_cap:** None
* **sliding_window_size:** 4096
* **use_sliding_window_attention:** False

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.