# deberta-v3-large-zeroshot-v1.1-all-33 by Moritz Borrett-Laurer Source: https://savrn.com/models/deberta-v3-large-zeroshot-v1-1-all-33 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve deberta-v3-large-zeroshot-v1.1-all-33 (435M 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 | 0.9 GB | 1.0 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 | | 8-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 | | 4-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 | 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 25, 2026. [deberta-v3-large-zeroshot-v1.1-all-33 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/deberta-v3-large-zeroshot-v1-1-all-33/gpus) ## Model Card By Moritz Borrett-Laurer (formerly Laurer), published under mit, revision c5dca3bda16d. ### Model description: deberta-v3-large-zeroshot-v1.1-all-33 The model is designed for zero-shot classification with the Hugging Face pipeline. The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. not_entailment). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into this task. A detailed description of how the model was trained and how it can be used is available in this [paper](https://savrn.com/papers/building-efficient-universal-classifiers-with-natural-language-inference). ### Training data [Read the full model card (5,529 words)](https://savrn.com/models/deberta-v3-large-zeroshot-v1-1-all-33/card) ## Configuration Architecture DebertaV2ForSequenceClassification Context length (tokens) 512 Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 128,100 Stored precision float16 Model type deberta-v2 ## Identity and Version Repository MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 Publisher Moritz Borrett-Laurer (formerly Laurer) Task Zero-shot classification Modality Text Library transformers Parameters 435M parameters Languages en Revision c5dca3bda16d30337e493e3e3e5caa19a3e7c8c2 First published 2023-11-27 Last updated 2024-04-01 ## Files and Weights 14 files, 4.1 GB in total. The weights are 5 files totalling 4.1 GB in bin, onnx, safetensors. Weights5 files · 4.1 GB Configuration3 files · 1.2 KB Tokenizer2 files · 8.6 MB Documentation2 files · 65.8 KB Other1 file · 2.5 MB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 870.2 MB | 0b3b2a29e2be | | onnx/model.onnx | Weights | 1.7 GB | 0e7f373de278 | | onnx/model_quantized.onnx | Weights | 642.6 MB | 9b90a764fba7 | | pytorch_model.bin | Weights | 870.3 MB | 4def21feccad | | training_args.bin | Weights | 4.2 KB | c3d5d8229f3a | | added_tokens.json | Configuration | 23 B | — | | config.json | Configuration | 1.0 KB | — | | special_tokens_map.json | Configuration | 173 B | — | | LICENSE | Documentation | 1.0 KB | — | | README.md | Documentation | 64.8 KB | — | | spm.model | Other | 2.5 MB | c679fbf93643 | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 8.6 MB | — | | tokenizer_config.json | Tokenizer | 384 B | — | ## License and Download License mit Access Open weights, no gate Download size 4.1 GB [Download from Moritz Borrett-Laurer (formerly Laurer)](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33) Released by Moritz Borrett-Laurer (formerly Laurer) through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:2312.17543](https://savrn.com/papers/building-efficient-universal-classifiers-with-natural-language-inference) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 4.1 GB | | 16-bit | 0.9 GB | | 8-bit | 0.4 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare deberta-v3-large-zeroshot-v1.1-all-33 - [bart-large-mnli vs deberta-v3-large-zeroshot-v1.1-all-33](https://savrn.com/models/compare/bart-large-mnli-vs-deberta-v3-large-zeroshot-v1-1-all-33) ## Questions About deberta-v3-large-zeroshot-v1.1-all-33 ### How much GPU memory does deberta-v3-large-zeroshot-v1.1-all-33 need? About 1 GB at 16-bit and 0.3 GB at 4-bit: the weights (435M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run deberta-v3-large-zeroshot-v1.1-all-33 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 deberta-v3-large-zeroshot-v1.1-all-33 commercially? Yes. deberta-v3-large-zeroshot-v1.1-all-33 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. ### What is deberta-v3-large-zeroshot-v1.1-all-33's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Zero-shot classification ### [deberta-v3-large-zeroshot-v2.0](https://savrn.com/models/deberta-v3-large-zeroshot-v2-0) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection. The main update of this zeroshot-v2.0 series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements. These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is… Open weights mit 435M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-large-zeroshot-v2-0) Model · Zero-shot classification ### [deberta-v3-large-zeroshot-v1](https://savrn.com/models/deberta-v3-large-zeroshot-v1) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) The model is designed for zero-shot classification with the Hugging Face pipeline. The model should be substantially better at zero-shot classification than my other zero-shot models on the The model can do one universal task: determine whether a hypothesis is true or nottrue given a text (also called entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into the task. The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format. 1. 26 classification tasks with ~400k texts: 'amazonpolarity', 'imdb', 'appreviews'… Open weights mit 435M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-large-zeroshot-v1) Model · Zero-shot classification ### [deberta-v3-large-zeroshot-v2.0-c](https://savrn.com/models/deberta-v3-large-zeroshot-v2-0-c) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection. The main update of this zeroshot-v2.0 series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements. These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is… Open weights mit 435M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-large-zeroshot-v2-0-c) Model · Zero-shot classification ### [DeBERTa-v3-large-mnli-fever-anli-ling-wanli](https://savrn.com/models/deberta-v3-large-mnli-fever-anli-ling-wanli) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the ANLI benchmark. The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which… Open weights mit 435M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-large-mnli-fever-anli-ling-wanli) Model · Zero-shot classification ### [nli-deberta-v3-large](https://savrn.com/models/nli-deberta-v3-large) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification Open weights apache-2.0 435M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/nli-deberta-v3-large) Model · Zero-shot classification ### [deberta-v3-large-tasksource-nli](https://savrn.com/models/deberta-v3-large-tasksource-nli) [Damien Sileo](https://savrn.com/model-publishers/sileod) DeBERTa-v3-large fine-tuned with multi-task learning on 600 tasks of the tasksource collection You can further fine-tune this model to use it for any classification or multiple-choice task. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 77% on WNLI). The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training. This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder. Each task had a specific CLS… Open weights apache-2.0 435M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-large-tasksource-nli) ## Moritz Borrett-Laurer (formerly Laurer) [All models and datasets](https://savrn.com/model-publishers/moritzlaurer) ## Versions - [c5dca3bda16d](https://savrn.com/models/deberta-v3-large-zeroshot-v1-1-all-33/versions/c5dca3bda16d) · current 2026-09-18 ## Explore More - [All zero-shot classification models](https://savrn.com/models/tasks/zero-shot-classification) - [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/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33) - [How the hub is built](https://savrn.com/model-hub/methodology)