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| Model | T5Gemma Series | Architecture | Model Size (GB) | Saved VRAM | Note |
|---|---|---|---|---|---|
| 270m-270m | V2 (Gemma-3) | Multimodal | 1.37 | ~13% | Vision tower takes up most of the full model |
| 1b-1b | V2 (Gemma-3) | Multimodal | 2.84 | ~33% | |
| 2b-2b | V1 (Gemma-2) | Text-only | 5.23 | ~53% | Most VRAM saved of V1 |
| 4b-4b | V2 (Gemma-3) | Multimodal | 8.60 | ~43% | Most VRAM saved of V2 |
| 9b-2b | V1 (Gemma-2) | Text-only | 18.46 | ~25% | Encoder takes up most of the full model |
1from transformers import T5GemmaEncoderModel
2
3model_path = "PhatcatDK/t5gemma-9b-2b-ul2-encoder-only"
4
5# Load the weights
6model = T5GemmaEncoderModel.from_pretrained(
7 model_path,
8 torch_dtype="auto", # Recommended: bfloat16
9 is_encoder_decoder=False, # CRITICAL: Tells Transformers there is no decoder
10 trust_remote_code=True
11)t5gemma-2b-2b-ul2 encoder within this ecosystem. This specific repository was created to round out a standardized suite (270m through 9b) with a "clean-config" setup, ensuring consistent architectural parity for cross-attention testing.