Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
t5
text2text-generation
summarization
text-generation-inference
Instructions to use google-t5/t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-t5/t5-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="google-t5/t5-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base") - Inference
- Notebooks
- Google Colab
- Kaggle
TMT: dynamic graph attention beats Mamba on WikiText-2 at 48% compute — open source
#41
by vigneshwar234 - opened
TemporalMesh Transformer — benchmarked on this dataset
TMT achieves 29.4 PPL on WikiText-2 (vs 31.8 Mamba, 42.1 vanilla) and 36.1 on WikiText-103 at only 48% relative compute — 120M params, 3 seeds.
Five innovations: Mesh Attention (O(S·k) dynamic kNN), Temporal Decay, Adaptive Exit Gate, Dual-Stream FFN, EMA Memory Anchors.
📄 Paper: https://zenodo.org/records/20287390
💻 Code: https://github.com/vignesh2027/TemporalMesh-Transformer
🎮 Demo: https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo