# nli-deberta-v3-small by Sentence Transformers - Cross-Encoders Source: https://savrn.com/models/nli-deberta-v3-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve nli-deberta-v3-small (142M 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 | 0.3 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 | | 8-bit | 0.1 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 | | 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 23, 2026. [nli-deberta-v3-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/nli-deberta-v3-small/gpus) ## SAVRN's Notes on nli-deberta-v3-small For every sentence pair you hand it, this cross-encoder returns three scores, one each for contradiction, entailment and neutral, which is the basis for its zero-shot classification use. Six layers and 142M parameters keep memory small: 0.3 GB at 16-bit, 0.2 GB at 8-bit, 0.1 GB at 4-bit. The cheapest setup on our Index is one MI300X with 192 GB at $1.85 per hour on-demand, which leaves almost the whole card idle, so share it. The download is another matter: 20 files and 4.45 GB across safetensors, ONNX and PyTorch formats. Apache 2.0 terms apply: commercial use, modification and redistribution, with the notices and NOTICE file kept, significant changes stated, and an express patent grant included. Check the 512 token context and the lineage: it derives from microsoft/deberta-v3-small and was trained on SNLI and MultiNLI. No evaluation figures are listed here, so test it on your own pairs. SAVRN Research, 2026-09-18 ## Model Card By Sentence Transformers - Cross-Encoders, published under apache-2.0, revision fa2804872c3b. ### Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-small](https://savrn.com/models/deberta-v3-small) ### Training Data The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. ### Performance - Accuracy on SNLI-test dataset: 91.65 - Accuracy on MNLI mismatched set: 87.55 For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli). ### Usage Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-deberta-v3-small') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) #Convert scores to labels label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] ``` ### Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): [Read the full model card (254 words)](https://savrn.com/models/nli-deberta-v3-small/card) ## Configuration Architecture DebertaV2ForSequenceClassification Context length (tokens) 512 Layers 6 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 128,100 Stored precision float32 Model type deberta-v2 ## Identity and Version Repository cross-encoder/nli-deberta-v3-small Publisher Sentence Transformers - Cross-Encoders Task Zero-shot classification Modality Text Library sentence-transformers Parameters 142M parameters Languages en Revision fa2804872c3b4bd748f38c0185cc85775361e735 First published 2022-03-02 Last updated 2025-04-15 ## Files and Weights 20 files, 4.5 GB in total. The weights are 11 files totalling 4.4 GB in bin, onnx, safetensors. Weights11 files · 4.4 GB Configuration3 files · 1.4 KB Tokenizer2 files · 8.7 MB Documentation1 file · 2.9 KB Other2 files · 2.5 MB Repository1 file · 1.2 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 567.6 MB | ebc79588dd73 | | onnx/model.onnx | Weights | 568.0 MB | 59fd8dd78926 | | onnx/model_O1.onnx | Weights | 585.2 MB | 2755d9ae55f8 | | onnx/model_O2.onnx | Weights | 585.1 MB | baabab86ef26 | | onnx/model_O3.onnx | Weights | 585.1 MB | 5791ade52d31 | | onnx/model_O4.onnx | Weights | 292.7 MB | 0f7bd2134d37 | | onnx/model_qint8_arm64.onnx | Weights | 172.5 MB | a1da6815c8da | | onnx/model_qint8_avx512.onnx | Weights | 172.5 MB | a1da6815c8da | | onnx/model_qint8_avx512_vnni.onnx | Weights | 172.5 MB | a1da6815c8da | | onnx/model_quint8_avx2.onnx | Weights | 172.5 MB | 03c2221313dc | | pytorch_model.bin | Weights | 567.6 MB | 2930841dcaa8 | | added_tokens.json | Configuration | 26 B | — | | config.json | Configuration | 1.1 KB | — | | special_tokens_map.json | Configuration | 301 B | — | | README.md | Documentation | 2.9 KB | — | | CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv | Other | 678 B | — | | spm.model | Other | 2.5 MB | c679fbf93643 | | .gitattributes | Repository | 1.2 KB | — | | tokenizer.json | Tokenizer | 8.7 MB | — | | tokenizer_config.json | Tokenizer | 1.3 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 4.4 GB [Download from Sentence Transformers - Cross-Encoders](https://huggingface.co/cross-encoder/nli-deberta-v3-small) Released by Sentence Transformers - Cross-Encoders through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [microsoft/deberta-v3-small](https://savrn.com/models/deberta-v3-small) - Quantized from [microsoft/deberta-v3-small](https://savrn.com/models/deberta-v3-small) - Trained on (disclosed) nyu-mll/multi_nli - Trained on (disclosed) [stanfordnlp/snli](https://savrn.com/datasets/snli) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 4.4 GB | | 16-bit | 0.3 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Quantized from[nli-deberta-v3-small](https://savrn.com/models/nli-deberta-v3-small-2) - Derived from[nli-deberta-v3-small](https://savrn.com/models/nli-deberta-v3-small-2) ## Questions About nli-deberta-v3-small ### How much GPU memory does nli-deberta-v3-small need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (142M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run nli-deberta-v3-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 nli-deberta-v3-small commercially? Yes. nli-deberta-v3-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. ### What is nli-deberta-v3-small's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Zero-shot classification ### [deberta-small-long-nli](https://savrn.com/models/deberta-small-long-nli) [Tasksource](https://savrn.com/model-publishers/tasksource) DeBERTa-v3-small with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli). Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks. This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for: - Zero-shot entailment-based classification for arbitrary labels [ZS]. - Natural language inference [NLI] - Further fine-tuning on a new task or… Open weights apache-2.0 142M parameters 1,680 tokens transformers [View model](https://savrn.com/models/deberta-small-long-nli) Model · Zero-shot classification ### [multilingual-zeroshot-small](https://savrn.com/models/multilingual-zeroshot-small) [Horizon Labs](https://savrn.com/model-publishers/horizon-labs) Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score… Open weights apache-2.0 141M parameters 8,192 tokens transformers [View model](https://savrn.com/models/multilingual-zeroshot-small) Model · Zero-shot classification ### [gliclass-small-v1.0](https://savrn.com/models/gliclass-small-v1-0) [Knowledgator Engineering](https://savrn.com/model-publishers/knowledgator) This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path. It can be used for topic classification, sentiment analysis and as a reranker in RAG pipelines. The model was trained on synthetic data and can be used in commercial applications. First of all, you need to install GLiClass library: Than you need to initialize a model and a pipeline: Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting. Below you can find a comparison… Open weights apache-2.0 144M parameters transformers [View model](https://savrn.com/models/gliclass-small-v1-0) Model · Zero-shot classification ### [nli-deberta-base](https://savrn.com/models/nli-deberta-base) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained using SentenceTransformers Cross-Encoder class. 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 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 139M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/nli-deberta-base) Model · Zero-shot classification ### [ModernBERT-base-nli](https://savrn.com/models/modernbert-base-nli) [Tasksource](https://savrn.com/model-publishers/tasksource) ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table). This is the equivalent of an "instruct" version. The model was trained for 200k steps on an Nvidia A30 GPU. It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels. The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top. Further gains can be obtained by fine-tuning on a single-task, e.g. SST, but it… Open weights apache-2.0 150M parameters 2,048 tokens transformers [View model](https://savrn.com/models/modernbert-base-nli) Model · Zero-shot classification ### [nli-roberta-base](https://savrn.com/models/nli-roberta-base) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained using SentenceTransformers Cross-Encoder class. 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 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 125M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/nli-roberta-base) ## Sentence Transformers - Cross-Encoders [All models and datasets](https://savrn.com/model-publishers/cross-encoder) ## Versions - [fa2804872c3b](https://savrn.com/models/nli-deberta-v3-small/versions/fa2804872c3b) · current 2026-09-18 ## Explore More - [All zero-shot classification models](https://savrn.com/models/tasks/zero-shot-classification) - [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. - [Sentence Transformers - Cross-Encoders website](https://sbert.net/) - [Hugging Face record](https://huggingface.co/cross-encoder/nli-deberta-v3-small) - [How the hub is built](https://savrn.com/model-hub/methodology)