# minilm-uncased-squad2 by Deepset: Open-Weight Model Source: https://savrn.com/models/minilm-uncased-squad2 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve minilm-uncased-squad2 (33M 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](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 | 0.0 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 | | 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 23, 2026. [minilm-uncased-squad2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/minilm-uncased-squad2/gpus) ## Model Card By Deepset, published under cc-by-4.0, revision 934656cdda79. ### MiniLM-L12-H384-uncased for Extractive QA ### Overview Language model: microsoft/MiniLM-L12-H384-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) Infrastructure: 1x Tesla v100 ### Hyperparameters ``` seed=42 batch_size = 12 n_epochs = 4 base_LM_model = "microsoft/MiniLM-L12-H384-uncased" max_seq_len = 384 learning_rate = 4e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 grad_acc_steps=4 ``` ### Usage #### In Haystack 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](https://github.com/deepset-ai/haystack/): [Read the full model card (386 words)](https://savrn.com/models/minilm-uncased-squad2/card) ## Configuration Architecture BertForQuestionAnswering Context length (tokens) 512 Layers 12 Hidden size 384 Feed-forward size 1,536 Attention heads 12 Vocabulary size 30,522 Model type bert ## Identity and Version Repository deepset/minilm-uncased-squad2 Publisher Deepset Task Question answering Modality Text Library transformers Parameters 33M parameters Languages en Revision 934656cdda79824eabf503ed56e15c01ddbdbe3f First published 2022-03-02 Last updated 2024-09-26 ## Files and Weights 10 files, 400.1 MB in total. The weights are 4 files totalling 399.8 MB in bin, msgpack, safetensors. Weights4 files · 399.8 MB Configuration2 files · 589 B Tokenizer2 files · 231.6 KB Documentation1 file · 6.1 KB Repository1 file · 445 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 132.9 MB | bcd4d6e16c9e | | model.safetensors | Weights | 133.5 MB | 240478c7251d | | pytorch_model.bin | Weights | 133.5 MB | d8582ee8eec6 | | training_args.bin | Weights | 1.5 KB | 1ce3a6d0039a | | config.json | Configuration | 477 B | — | | special_tokens_map.json | Configuration | 112 B | — | | README.md | Documentation | 6.1 KB | — | | .gitattributes | Repository | 445 B | — | | tokenizer_config.json | Tokenizer | 107 B | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License cc-by-4.0 Access Open weights, no gate Download size 399.8 MB [Download from Deepset](https://huggingface.co/deepset/minilm-uncased-squad2) Released by Deepset through its official repository on Hugging Face. [Read the license](https://creativecommons.org/licenses/by/4.0/). ## Built From - 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 | | --- | --- | --- | --- | --- | --- | | squad_v2 | Configuration squad_v2Task Question AnsweringMetric Exact MatchComparison conditions not established | 76.1921 | [deepset](https://huggingface.co/deepset/minilm-uncased-squad2) Publisher reported | Evaluated revision not stated | — | | squad_v2 | Configuration squad_v2Task Question AnsweringMetric F1Comparison conditions not established | 79.5483 | [deepset](https://huggingface.co/deepset/minilm-uncased-squad2) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 399.8 MB | | 16-bit | 0.1 GB | | 8-bit | 0.0 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Derived from[rai](https://savrn.com/models/rai) ## Questions About minilm-uncased-squad2 ### How much GPU memory does minilm-uncased-squad2 need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (33M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run minilm-uncased-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 minilm-uncased-squad2 commercially? Yes. minilm-uncased-squad2 is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated. ### What is minilm-uncased-squad2's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Question answering ### [rai](https://savrn.com/models/rai) [Prayas Abhinav](https://savrn.com/model-publishers/prayasabhinav) What it is. An extractive question-answering model. Given a question and a short typed answer, it returns the phrase in the answer that answers the question, or nothing. It cannot generate text. It is the reading part of rai, a tool that says only whether a description is complete under a notion of completeness written down in advance. Every decision after the reading is made in plain code. Trained from. deepset/minilm-uncased-squad2 (CC-BY-4.0), deepset's fine-tune of Microsoft's MiniLM-L12-H384-uncased (MIT) on SQuAD 2.0. Same architecture, 33M parameters, nothing added. Credit to deepset and Microsoft. 0.3.7 trains on 0.3.6's rows and 5,800 more written in Claude Code. In 0.3.6's rows… Open weights cc-by-4.0 33M parameters 512 tokens transformers [View model](https://savrn.com/models/rai) Model · Question answering ### [basic-chat-model](https://savrn.com/models/basic-chat-model) [Lenin Villanueva](https://savrn.com/model-publishers/leninangelov) This modelcard aims to be a base template for new models. It has been generated using this raw template. 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 apache-2.0 31M parameters [View model](https://savrn.com/models/basic-chat-model) Model · Question answering ### [bert-medium-squad2-distilled](https://savrn.com/models/bert-medium-squad2-distilled) [Deepset](https://savrn.com/model-publishers/deepset) Haystack version 1.x distillation feature was used for training. deepset/bert-large-uncased-whole-word-masking-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. - 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 mit 41M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-medium-squad2-distilled) Model · Question answering ### [mobilebert-uncased-squad-v2](https://savrn.com/models/mobilebert-uncased-squad-v2) [Qingqing Cao](https://savrn.com/model-publishers/csarron) MobileBERT is a thin version of BERTLARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. This model was fine-tuned from the HuggingFace checkpoint google/mobilebert-uncased on SQuAD2.0. CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz Memory: 32 GiB GPUs: 2 GeForce GTX 1070, each with 8GiB memory GPU driver: 418.87.01, CUDA: 10.1 It took about 3.5 hours to finish. Note that the above results didn't involve any hyperparameter search. Open weights mit 25M parameters 512 tokens transformers [View model](https://savrn.com/models/mobilebert-uncased-squad-v2) Model · Question answering ### [mobilebert-uncased-squad-v1](https://savrn.com/models/mobilebert-uncased-squad-v1) [Qingqing Cao](https://savrn.com/model-publishers/csarron) MobileBERT is a thin version of BERTLARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. This model was fine-tuned from the HuggingFace checkpoint google/mobilebert-uncased on SQuAD1.1. CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz Memory: 32 GiB GPUs: 2 GeForce GTX 1070, each with 8GiB memory GPU driver: 418.87.01, CUDA: 10.1 It took about 3 hours to finish. Note that the above results didn't involve any hyperparameter search. Open weights mit 25M parameters 512 tokens transformers [View model](https://savrn.com/models/mobilebert-uncased-squad-v1) Model · Question answering ### [koelectra-small-v2-distilled-korquad-384](https://savrn.com/models/koelectra-small-v2-distilled-korquad-384) [Jangwon Park](https://savrn.com/model-publishers/monologg) Open weights 14M parameters 512 tokens transformers [View model](https://savrn.com/models/koelectra-small-v2-distilled-korquad-384) ## Deepset [All models and datasets](https://savrn.com/model-publishers/deepset) ## Versions - [934656cdda79](https://savrn.com/models/minilm-uncased-squad2/versions/934656cdda79) · current 2026-09-18 ## Explore More - [All question answering models](https://savrn.com/models/tasks/question-answering) - [All models under cc-by-4.0](https://savrn.com/models/licenses/cc-by-4-0) - [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 2026-10-21. - [Hugging Face record](https://huggingface.co/deepset/minilm-uncased-squad2) - [How the hub is built](https://savrn.com/model-hub/methodology)