Instructions to use Kemsekov/gemma-2-2b-ru-doc-spellfix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use Kemsekov/gemma-2-2b-ru-doc-spellfix with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://Kemsekov/gemma-2-2b-ru-doc-spellfix") - Keras
How to use Kemsekov/gemma-2-2b-ru-doc-spellfix with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Kemsekov/gemma-2-2b-ru-doc-spellfix") - Notebooks
- Google Colab
- Kaggle
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: 26
- num_query_heads: 8
- num_key_value_heads: 4
- hidden_dim: 2304
- intermediate_dim: 18432
- head_dim: 256
- layer_norm_epsilon: 1e-06
- dropout: 0
- query_head_dim_normalize: True
- use_post_ffw_norm: True
- use_post_attention_norm: True
- final_logit_soft_cap: 30.0
- attention_logit_soft_cap: 50.0
- sliding_window_size: 4096
- use_sliding_window_attention: True
This model card has been generated automatically and should be completed by the model author. See Model Cards documentation for more information.
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