metadata
library_name: keras-nlp
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 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
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