# flan-t5-small by Google: Open-Weight Model Source: https://savrn.com/models/flan-t5-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve flan-t5-small (77M 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 | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [flan-t5-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/flan-t5-small/gpus) ## SAVRN's Notes on flan-t5-small Do not size the card by the download. The repository weighs about 1.37 GB across 12 files because it ships in four formats, safetensors, pytorch, jax and tf, but the copy you load at 16-bit needs 0.2 GB, and 8-bit halves that to 0.1 GB. So this 77 million parameter T5ForConditionalGeneration model fits any accelerator you own; the cheapest setup we price, one MI300X with 192 GB at $1.85 an hour, leaves nearly all of that memory free. Apache 2.0 permits commercial deployment, modification and redistribution, provided the notices stay and significant changes are stated, with a patent grant included. Check two things: no context length is stated, so measure the input size you need yourself, and the last update was October 10, 2023. The training relations name gsm8k, aqua_rat, esnli, lambada and deepmind/code_contests, and Google describes tuning on more than 1,000 additional tasks beyond T5. SAVRN Research, 2026-09-18 ## Model Card By Google, published under apache-2.0, revision 0fc9ddf78a1e. ### Model Card for FLAN-T5 small ### Table of Contents 1. TL;DR 2. Model Details 3. Usage 4. Uses 5. Bias, Risks, and Limitations 6. Training Details 7. Evaluation 8. Environmental Impact 9. Citation 10. Model Card Authors ### TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). ### Model Details ### Model Description [Read the full model card (987 words)](https://savrn.com/models/flan-t5-small/card) ## Configuration Architecture T5ForConditionalGeneration Vocabulary size 32,128 Model type t5 ## Identity and Version Repository google/flan-t5-small Publisher Google Task Not stated by the source Modality Other Library transformers Parameters 77M parameters Languages en, fr, ro, de Revision 0fc9ddf78a1e988dac52e2dac162b0ede4fd74ab First published 2022-10-21 Last updated 2023-10-10 ## Files and Weights 12 files, 1.4 GB in total. The weights are 4 files totalling 1.4 GB in bin, h5, msgpack, safetensors. Weights4 files · 1.4 GB Configuration3 files · 3.7 KB Tokenizer3 files · 3.2 MB Documentation1 file · 10.8 KB Repository1 file · 1.4 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 307.9 MB | 46dc0494fa27 | | model.safetensors | Weights | 307.9 MB | 495fa51e2046 | | pytorch_model.bin | Weights | 307.9 MB | 4a8c0174fa3e | | tf_model.h5 | Weights | 439.8 MB | 9c951cf31dec | | config.json | Configuration | 1.4 KB | — | | generation_config.json | Configuration | 147 B | — | | special_tokens_map.json | Configuration | 2.2 KB | — | | README.md | Documentation | 10.8 KB | — | | .gitattributes | Repository | 1.4 KB | — | | spiece.model | Tokenizer | 791.7 KB | d60acb128cf7 | | tokenizer.json | Tokenizer | 2.4 MB | — | | tokenizer_config.json | Tokenizer | 2.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.4 GB [Download from Google](https://www.kaggle.com/models/google/flan-t5) Released by Google through Kaggle. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1910.09700](https://savrn.com/papers/quantifying-the-carbon-emissions-of-machine-learning) - Described by [arXiv:2210.11416](https://savrn.com/papers/scaling-instruction-finetuned-language-models) - Trained on (disclosed) aqua_rat - Trained on (disclosed) deepmind/code_contests - Trained on (disclosed) djaym7/wiki_dialog - Trained on (disclosed) esnli - Trained on (disclosed) gsm8k - Trained on (disclosed) lambada - Trained on (disclosed) qed - Trained on (disclosed) quasc - Trained on (disclosed) svakulenk0/qrecc - Trained on (disclosed) taskmaster2 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.4 GB | | 16-bit | 0.2 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. ## Questions About flan-t5-small ### How much GPU memory does flan-t5-small need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (77M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run flan-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 flan-t5-small commercially? Yes. flan-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. ## Google [All models and datasets](https://savrn.com/model-publishers/google) ## Versions - [0fc9ddf78a1e](https://savrn.com/models/flan-t5-small/versions/0fc9ddf78a1e) · current 2026-09-18 ## Explore More - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Google website](https://ai.google.dev/) - [Hugging Face record](https://huggingface.co/google/flan-t5-small) - [How the hub is built](https://savrn.com/model-hub/methodology)