source languages: fr; target languages: en; OPUS readme: fr-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
The developers of the Text-To-Text Transfer Transformer (T5) write: T5-Small is the checkpoint with 60 million parameters. The developers write in a blog post that the model: See the blog post and research paper for further details.
Parameters61M
Context—
Weights2.2 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads25M
Runs On
What it takes to serve t5-small (61M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By T5 community, published under apache-2.0, revision df1b051c4962.
Model Card for T5 Small
Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Environmental Impact
- Citation
- Model Card Authors
- How To Get Started With the Model
Model Details
Model Description
The developers of the Text-To-Text Transfer Transformer (T5) write:
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
T5-Small is the checkpoint with 60 million parameters.
Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 32,128
- Model type
- t5
Identity and Version
- Repository
- google-t5/t5-small
- Publisher
- T5 community
- Task
- Translation
- Modality
- Text
- Library
- transformers
- Parameters
- 61M parameters
- Languages
- en, fr, ro, de
- Revision
- df1b051c49625cf57a3d0d8d3863ed4d13564fe4
- First published
- 2022-03-02
- Last updated
- 2023-06-30
Files and Weights
20 files, 2.2 GB in total. The weights are 13 files totalling 2.2 GB in bin, h5, msgpack, onnx, ot, safetensors.
Weights13 files · 2.2 GB
Configuration2 files · 1.4 KB
Tokenizer3 files · 2.2 MB
Documentation1 file · 8.5 KB
Repository1 file · 537 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| flax_model.msgpack | Weights | 242.0 MB | b143e13ccb73 |
| model.safetensors | Weights | 242.0 MB | bd944e5f1b3a |
| onnx/decoder_model.onnx | Weights | 232.5 MB | 2256c6365e82 |
| onnx/decoder_model_merged.onnx | Weights | 232.6 MB | f6d2874dafd2 |
| onnx/decoder_model_merged_quantized.onnx | Weights | 58.7 MB | af6a06ea8488 |
| onnx/decoder_model_quantized.onnx | Weights | 58.4 MB | a1b7a03fd50c |
| onnx/decoder_with_past_model.onnx | Weights | 219.9 MB | 2eb7790f56c4 |
| onnx/decoder_with_past_model_quantized.onnx | Weights | 55.2 MB | 30e4b22fc986 |
| onnx/encoder_model.onnx | Weights | 141.4 MB | 3ebb633a3a81 |
| onnx/encoder_model_quantized.onnx | Weights | 35.5 MB | 5124770e67bf |
| pytorch_model.bin | Weights | 242.1 MB | dd8c1c79a54c |
| rust_model.ot | Weights | 242.1 MB | 636b17628823 |
| tf_model.h5 | Weights | 242.3 MB | fc9d2ec530ef |
| config.json | Configuration | 1.2 KB | — |
| generation_config.json | Configuration | 147 B | — |
| README.md | Documentation | 8.5 KB | — |
| .gitattributes | Repository | 537 B | — |
| spiece.model | Tokenizer | 791.7 KB | — |
| tokenizer.json | Tokenizer | 1.4 MB | — |
| tokenizer_config.json | Tokenizer | 2.3 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 2.2 GB
Download from T5 community
Released by T5 community through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:1606.05250
- Described by arXiv:1704.05426
- Described by arXiv:1708.00055
- Described by arXiv:1805.12471
- Described by arXiv:1808.09121
- Described by arXiv:1810.12885
- Described by arXiv:1905.10044
- Described by arXiv:1910.09700
- Trained on (disclosed) c4
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 2.2 GB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Compare t5-small
Questions About t5-small
How much GPU memory does t5-small need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (61M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run t5-small on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use t5-small commercially?
Yes. t5-small is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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