tiny-t5gemma-test

harshaljanjani/tiny-t5gemma-test

tiny-t5gemma-test is a text generation model from harshaljanjani. Use it when you need the model to write or continue text. The card lists the license as mit.

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At a glance

Task: Text Generation
License: mit
Model type: t5gemma
Access: Public
Created: Aug 25, 2025
Updated: Mar 3, 2026
SHA: 3e0cad8e

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Model card

From the Hugging Face model README

Model Summary

T5Gemma is a family of lightweight, open-weight language models built from the same research and technology used to create the Gemma models. Based on the T5 text-to-text architecture, T5Gemma is a decoder-only model designed for a wide range of natural language processing (NLP) tasks. It's trained on a massive corpus of text and code, enabling it to excel at tasks like summarization, question answering, and text generation. T5Gemma offers a balance of high performance and computational efficiency, making it suitable for both research and deployment in various applications.

Key Features:

Decoder-Only Architecture: A modern, efficient design based on the T5 framework.
Open Weights: Freely available for research, and commercial use, promoting accessibility and innovation.
Strong Performance: Achieves state-of-the-art results on NLP benchmarks for its size.
Multiple Sizes: Comes in various sizes (e.g., 2B and 7B parameters) to fit different hardware capabilities.
Text-to-Text Framework: Trained to transform an input text sequence into a target text sequence, making it versatile for many tasks.

Training Strategies:

T5Gemma is pre-trained on a large and diverse dataset of up to 6 trillion tokens of text and code from web documents, mathematics, and code. The training process uses a causal masking scheme tailored for decoder-only models, enabling it to learn complex patterns and generate coherent, contextually relevant text.

Weights are released under the Gemma Terms of Use. Keras model code is released under the Apache 2 License.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Available T5Gemma Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

Preset	Parameters	Description
t5gemma_s_s_ul2	312.5M	T5Gemma S/S model with a small encoder and small decoder, adapted as a UL2 model.
t5gemma_s_s_prefixlm	312.5M	T5Gemma S/S model with a small encoder and small decoder, adapted as a prefix language model.
t5gemma_s_s_ul2_it	312.5M	T5Gemma S/S model with a small encoder and small decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_s_s_prefixlm_it	312.5M	T5Gemma S/S model with a small encoder and small decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_b_b_ul2	591.5M	T5Gemma B/B model with a base encoder and base decoder, adapted as a UL2 model.
t5gemma_b_b_prefixlm	591.5M	T5Gemma B/B model with a base encoder and base decoder, adapted as a prefix language model.
t5gemma_b_b_ul2_it	591.5M	T5Gemma B/B model with a base encoder and base decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_b_b_prefixlm_it	591.5M	T5Gemma B/B model with a base encoder and base decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_l_l_ul2	1.24B	T5Gemma L/L model with a large encoder and large decoder, adapted as a UL2 model.
t5gemma_l_l_prefixlm	1.24B	T5Gemma L/L model with a large encoder and large decoder, adapted as a prefix language model.
t5gemma_l_l_ul2_it	1.24B	T5Gemma L/L model with a large encoder and large decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_l_l_prefixlm_it	1.24B	T5Gemma L/L model with a large encoder and large decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_ml_ml_ul2	2.20B	T5Gemma ML/ML model with a medium-large encoder and medium-large decoder, adapted as a UL2 model.
t5gemma_ml_ml_prefixlm	2.20B	T5Gemma ML/ML model with a medium-large encoder and medium-large decoder, adapted as a prefix language model.
t5gemma_ml_ml_ul2_it	2.20B	T5Gemma ML/ML model with a medium-large encoder and medium-large decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_ml_ml_prefixlm_it	2.20B	T5Gemma ML/ML model with a medium-large encoder and medium-large decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_xl_xl_ul2	3.77B	T5Gemma XL/XL model with an extra-large encoder and extra-large decoder, adapted as a UL2 model.
t5gemma_xl_xl_prefixlm	3.77B	T5Gemma XL/XL model with an extra-large encoder and extra-large decoder, adapted as a prefix language model.
t5gemma_xl_xl_ul2_it	3.77B	T5Gemma XL/XL model with an extra-large encoder and extra-large decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_xl_xl_prefixlm_it	3.77B	T5Gemma XL/XL model with an extra-large encoder and extra-large decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_2b_2b_ul2	5.60B	T5Gemma 2B/2B model with a 2-billion-parameter encoder and 2-billion-parameter decoder, adapted as a UL2 model.
t5gemma_2b_2b_prefixlm	5.60B	T5Gemma 2B/2B model with a 2-billion-parameter encoder and 2-billion-parameter decoder, adapted as a prefix language model.
t5gemma_2b_2b_ul2_it	5.60B	T5Gemma 2B/2B model with a 2-billion-parameter encoder and 2-billion-parameter decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_2b_2b_prefixlm_it	5.60B	T5Gemma 2B/2B model with a 2-billion-parameter encoder and 2-billion-parameter decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_9b_2b_ul2	12.29B	T5Gemma 9B/2B model with a 9-billion-parameter encoder and 2-billion-parameter decoder, adapted as a UL2 model.
t5gemma_9b_2b_prefixlm	12.29B	T5Gemma 9B/2B model with a 9-billion-parameter encoder and 2-billion-parameter decoder, adapted as a prefix language model.
t5gemma_9b_2b_ul2_it	12.29B	T5Gemma 9B/2B model with a 9-billion-parameter encoder and 2-billion-parameter decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_9b_2b_prefixlm_it	12.29B	T5Gemma 9B/2B model with a 9-billion-parameter encoder and 2-billion-parameter decoder, adapted as a prefix language model and fine-tuned for instruction following.
t5gemma_9b_9b_ul2	20.33B	T5Gemma 9B/9B model with a 9-billion-parameter encoder and 9-billion-parameter decoder, adapted as a UL2 model.
t5gemma_9b_9b_prefixlm	20.33B	T5Gemma 9B/9B model with a 9-billion-parameter encoder and 9-billion-parameter decoder, adapted as a prefix language model.
t5gemma_9b_9b_ul2_it	20.33B	T5Gemma 9B/9B model with a 9-billion-parameter encoder and 9-billion-parameter decoder, adapted as a UL2 model and fine-tuned for instruction following.
t5gemma_9b_9b_prefixlm_it	20.33B	T5Gemma 9B/9B model with a 9-billion-parameter encoder and 9-billion-parameter decoder, adapted as a prefix language model and fine-tuned for instruction following.

Task: Text Generation
Type: t5gemma
License: mit
Created: Aug 25, 2025
Updated: Mar 3, 2026