# flan-t5-base-squad2 by Sebastian Husch Lee: Open Model Source: https://savrn.com/models/flan-t5-base-squad2 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve flan-t5-base-squad2 (223M 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 | 0.4 GB | 0.5 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 | 0.2 GB | 0.3 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 | 0.1 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 | 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 22, 2026. [flan-t5-base-squad2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/flan-t5-base-squad2/gpus) ## Model Card By Sebastian Husch Lee, published under mit, revision c1fc232b3e8d. ### flan-t5-base for Extractive QA This is the [flan-t5-base](https://savrn.com/models/flan-t5-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. UPDATE: With transformers version 4.31.0 the use_remote_code=True is no longer necessary. NOTE: The token must be manually added to the beginning of the question for this model to work properly. It uses the token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually. ### Overview Language model: flan-t5-base Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Infrastructure: 1x NVIDIA 3070 ### Model Usage [Read the full model card (366 words)](https://savrn.com/models/flan-t5-base-squad2/card) ## Configuration Architecture T5ForQuestionAnswering Vocabulary size 32,128 Stored precision float32 Model type t5 ## Identity and Version Repository sjrhuschlee/flan-t5-base-squad2 Publisher Sebastian Husch Lee Task Question answering Modality Text Library transformers Parameters 223M parameters Languages en Revision c1fc232b3e8d745877fb1658a30627cf23629022 First published 2023-06-14 Last updated 2025-10-15 ## Files and Weights 11 files, 1.8 GB in total. The weights are 2 files totalling 1.8 GB in bin, safetensors. Weights2 files · 1.8 GB Configuration4 files · 13.0 KB Tokenizer3 files · 3.2 MB Documentation1 file · 6.4 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 891.7 MB | 91c789f42701 | | pytorch_model.bin | Weights | 891.7 MB | 112d93666dbe | | added_tokens.json | Configuration | 21 B | — | | config.json | Configuration | 1.6 KB | — | | modeling_t5qa.py | Configuration | 9.1 KB | — | | special_tokens_map.json | Configuration | 2.2 KB | — | | README.md | Documentation | 6.4 KB | — | | .gitattributes | Repository | 1.5 KB | — | | spiece.model | Tokenizer | 791.7 KB | d60acb128cf7 | | tokenizer.json | Tokenizer | 2.4 MB | — | | tokenizer_config.json | Tokenizer | 2.3 KB | — | ## License and Download License mit Access Open weights, no gate Download size 1.8 GB [Download from Sebastian Husch Lee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Released by Sebastian Husch Lee through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from [google/flan-t5-base](https://savrn.com/models/flan-t5-base) - Trained on (disclosed) squad - Trained on (disclosed) squad_v2 ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | adversarial_qa | Configuration adversarialQATask Question AnsweringMetric Exact MatchComparison conditions not established | 34.167 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | adversarial_qa | Configuration adversarialQATask Question AnsweringMetric F1Comparison conditions not established | 46.911 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad | Configuration plain_textTask Question AnsweringMetric Exact MatchComparison conditions not established | 86.367 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad | Configuration plain_textTask Question AnsweringMetric F1Comparison conditions not established | 92.965 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad_adversarial | Configuration AddOneSentTask Question AnsweringMetric Exact MatchComparison conditions not established | 80.862 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad_adversarial | Configuration AddOneSentTask Question AnsweringMetric F1Comparison conditions not established | 86.07 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad_v2 | Configuration squad_v2Task Question AnsweringMetric Exact MatchComparison conditions not established | 82.203 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squad_v2 | Configuration squad_v2Task Question AnsweringMetric F1Comparison conditions not established | 85.283 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts amazon | Configuration amazonTask Question AnsweringMetric Exact MatchComparison conditions not established | 71.624 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts amazon | Configuration amazonTask Question AnsweringMetric F1Comparison conditions not established | 85.113 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts new_wiki | Configuration new_wikiTask Question AnsweringMetric Exact MatchComparison conditions not established | 82.389 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts new_wiki | Configuration new_wikiTask Question AnsweringMetric F1Comparison conditions not established | 91.259 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts nyt | Configuration nytTask Question AnsweringMetric Exact MatchComparison conditions not established | 83.736 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts nyt | Configuration nytTask Question AnsweringMetric F1Comparison conditions not established | 91.675 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts reddit | Configuration redditTask Question AnsweringMetric Exact MatchComparison conditions not established | 72.743 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | | squadshifts reddit | Configuration redditTask Question AnsweringMetric F1Comparison conditions not established | 84.273 | [sjrhuschlee](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.8 GB | | 16-bit | 0.4 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About flan-t5-base-squad2 ### How much GPU memory does flan-t5-base-squad2 need? About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (223M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run flan-t5-base-squad2 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-base-squad2 commercially? Yes. flan-t5-base-squad2 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ## Similar Models Model · Question answering ### [deberta-v3-base-squad2](https://savrn.com/models/deberta-v3-base-squad2) [Deepset](https://savrn.com/model-publishers/deepset) This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. Timo Möller: timo.moeller [at] deepset.ai deepset is the company behind the production-ready open-source AI framework Haystack. We also… Open weights cc-by-4.0 184M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-base-squad2) Model · Question answering ### [bert-multi-cased-finetuned-xquadv1](https://savrn.com/models/bert-multi-cased-finetuned-xquadv1) [Manuel Romero](https://savrn.com/model-publishers/mrm8488) This model was created by Google and fine-tuned on XQuAD like data for multilingual (11 different languages) Q&A downstream task. Deepmind XQuAD As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this setting so that models can focus on cross-lingual transfer. We show the average number of tokens per paragraph, question, and answer for each language in the table below. The statistics were obtained using Jieba for Chinese and the Moses tokenizer for the other languages. As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to… Open weights 178M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-multi-cased-finetuned-xquadv1) Model · Question answering ### [XLM-R-Base-squad-en-15K-s42-LoRA-mrg-v260920193856](https://savrn.com/models/xlm-r-base-squad-en-15k-s42-lora-mrg-v260920193856) [Alim](https://savrn.com/model-publishers/alxxtexxr) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 277M parameters 514 tokens transformers [View model](https://savrn.com/models/xlm-r-base-squad-en-15k-s42-lora-mrg-v260920193856) Model · Question answering ### [xlm-roberta-base-squad2](https://savrn.com/models/xlm-roberta-base-squad2) [Deepset](https://savrn.com/model-publishers/deepset) Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. Evaluated on the SQuAD 2.0 dev set with the official eval script. "exact": 33.67279167589108 "total": 4517 "exact": 48.739495798319325 "total": 1190 Timo Möller: timo.moeller [at] deepset.ai deepset is the company behind the production-ready open-source AI framework Haystack. We also have a Discord community open to… Open weights cc-by-4.0 277M parameters 514 tokens transformers [View model](https://savrn.com/models/xlm-roberta-base-squad2) Model · Question answering ### [xlm-roberta-base-squad2-distilled](https://savrn.com/models/xlm-roberta-base-squad2-distilled) [Deepset](https://savrn.com/model-publishers/deepset) Haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. Evaluated on the SQuAD 2.0 dev set Timo Möller: timo.moeller@deepset.ai deepset is the company behind the production-ready open-source AI framework Haystack. We also have a Discord community open to… Open weights mit 277M parameters 514 tokens transformers [View model](https://savrn.com/models/xlm-roberta-base-squad2-distilled) Model · Question answering ### [mdeberta-v3-base-squad2](https://savrn.com/models/mdeberta-v3-base-squad2) [Tim](https://savrn.com/model-publishers/timpal0l) It has been finetuned for 3 epochs on SQuAD2.0. DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates. mDeBERTa is multilingual version of DeBERTa… Open weights mit 278M parameters 512 tokens transformers [View model](https://savrn.com/models/mdeberta-v3-base-squad2) ## Sebastian Husch Lee [All models and datasets](https://savrn.com/model-publishers/sjrhuschlee) ## Versions - [c1fc232b3e8d](https://savrn.com/models/flan-t5-base-squad2/versions/c1fc232b3e8d) · current 2026-09-18 ## Explore More - [All question answering models](https://savrn.com/models/tasks/question-answering) - [All models under mit](https://savrn.com/models/licenses/mit) - [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 2026-10-21. - [Hugging Face record](https://huggingface.co/sjrhuschlee/flan-t5-base-squad2) - [How the hub is built](https://savrn.com/model-hub/methodology)