# ModernBERT-large-nli by Tasksource: Open-Weight Model Source: https://savrn.com/models/modernbert-large-nli Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve ModernBERT-large-nli (396M 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 | 0.8 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 | 0.2 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 | 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. [ModernBERT-large-nli on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/modernbert-large-nli/gpus) ## Model Card By Tasksource, published under apache-2.0, revision ca476cb923a8. ### Model Card for Model ID This model is 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 this checkpoint is great for zero-shot classification and natural language inference (contradiction/entailment/neutral classification). [Read the full model card (527 words)](https://savrn.com/models/modernbert-large-nli/card) ## Configuration Architecture ModernBertForSequenceClassification Context length (tokens) 2,048 Layers 28 Hidden size 1,024 Feed-forward size 2,624 Attention heads 16 Vocabulary size 50,368 Stored precision float32 Model type modernbert ## Identity and Version Repository tasksource/ModernBERT-large-nli Publisher Tasksource Task Zero-shot classification Modality Text Library transformers Parameters 396M parameters Languages en Revision ca476cb923a8637073d4ceb0f19f7fc236e260d4 First published 2025-01-04 Last updated 2025-01-04 ## Files and Weights 7 files, 1.6 GB in total. The weights are 1 file totalling 1.6 GB in safetensors. Weights1 file · 1.6 GB Configuration2 files · 6.3 KB Tokenizer2 files · 3.6 MB Documentation1 file · 9.9 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.6 GB | f56738ff6d17 | | config.json | Configuration | 5.6 KB | — | | special_tokens_map.json | Configuration | 694 B | — | | README.md | Documentation | 9.9 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 3.6 MB | — | | tokenizer_config.json | Tokenizer | 20.9 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.6 GB [Download from Tasksource](https://huggingface.co/tasksource/ModernBERT-large-nli) Released by Tasksource through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [answerdotai/ModernBERT-large](https://savrn.com/models/modernbert-large) - Trained on (disclosed) [facebook/anli](https://savrn.com/datasets/anli) - Trained on (disclosed) [nyu-mll/glue](https://savrn.com/datasets/glue) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.6 GB | | 16-bit | 0.8 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 ModernBERT-large-nli - [bart-large-mnli vs ModernBERT-large-nli](https://savrn.com/models/compare/bart-large-mnli-vs-modernbert-large-nli) ## Questions About ModernBERT-large-nli ### How much GPU memory does ModernBERT-large-nli need? About 1 GB at 16-bit and 0.2 GB at 4-bit: the weights (396M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run ModernBERT-large-nli 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 ModernBERT-large-nli commercially? Yes. ModernBERT-large-nli 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 ModernBERT-large-nli's context length? 2,048 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Zero-shot classification ### [finecat-nli-l](https://savrn.com/models/finecat-nli-l) [Lee Miller](https://savrn.com/model-publishers/dleemiller) This model is a fine-tune of the excellent tasksource/ModernBERT-large-nli, trained on the dleemiller/FineCat-NLI dataset—a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. The training also incorporates logit distillation from MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli, a top-performing NLI model, particularly on ANLI benchmarks. \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}{\text{CE}}(z^{(s)}, y) + \beta \cdot \mathcal{L}{\text{MSE}}(z^{(s)}, z^{(t)}) \end{equation} where \\(z^{(s)}\\) and \\(z^{(t)}\\) are the student and teacher logits, \\(y\\) are the ground truth labels, and… Open weights 396M parameters 2,048 tokens sentence-transformers [View model](https://savrn.com/models/finecat-nli-l) Model · Zero-shot classification ### [finecat-nli-v2-l](https://savrn.com/models/finecat-nli-v2-l) [Lee Miller](https://savrn.com/model-publishers/dleemiller) This model is a fine-tune of jhu-clsp/ettin-encoder-400m, trained on the dleemiller/FineCat-NLI-Reason dataset, a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. 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You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned facebook/bart-large-mnli, here. This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use hypothesistemplate="This text is about {}." as this is the template used during fine-tuning. For settings other than topic classification, you can use any model pre-trained on MNLI such as facebook/bart-large-mnli… Open weights apache-2.0 407M parameters 1,024 tokens transformers [View model](https://savrn.com/models/bart-large-mnli-yahoo-answers) Model · Zero-shot classification ### [bart-large-mnli](https://savrn.com/models/bart-large-mnli) [AI at Meta](https://savrn.com/model-publishers/facebook) This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset. - The bart-large model page - BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of This text is about politics.. The probabilities for entailment and contradiction are then converted… Open weights mit 407M parameters 1,024 tokens transformers [View model](https://savrn.com/models/bart-large-mnli) Model · Zero-shot classification ### [nitzotz](https://savrn.com/models/nitzotz) [Netanel Elyasi](https://savrn.com/model-publishers/brainboxai) What it is. Nitzotz reads a Hebrew message and answers questions you type about it: pick one of several options, give a score on a scale, or say yes or no to a claim. For every answer it gives a probability you can trust, so you know when it is sure and when it is guessing. It does not write text, so it cannot make things up. It runs on a normal laptop, with no internet connection and no cost per question. What it is for. Deciding what to do with incoming messages: is this a scam, what kind of message is it, which department should get it, how urgent is it. It is not a chatbot and it is not built for long documents (see Limitations). In numbers. On 298 Hebrew messages it says correctly… Open weights apache-2.0 384M parameters laya [View model](https://savrn.com/models/nitzotz) Model · Zero-shot classification ### [finecat-nli-ultra-l](https://savrn.com/models/finecat-nli-ultra-l) [Lee Miller](https://savrn.com/model-publishers/dleemiller) This model is a fine-tune of dmis-lab/UltraBERT-380M, trained on the dleemiller/FineCat-NLI-Reason dataset, a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. Also includes additional NLI, fact verification and long document sources (2.05M pairs in total). The training uses logit distillation from an NLI finetune of Qwen3.8-27B. \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}{\text{CE}}(z^{(s)}, y) + \beta \cdot T^2 \cdot \mathrm{KL}\left(\sigma(z^{(t)}/T) \,\|\, \sigma(z^{(s)}/T)\right) \end{equation} where \\(z^{(s)}\\) and \\(z^{(t)}\\) are the student and teacher logits, \\(y\\) are the ground truth… Open weights apache-2.0 378M parameters sentence-transformers [View model](https://savrn.com/models/finecat-nli-ultra-l) ## Tasksource [All models and datasets](https://savrn.com/model-publishers/tasksource) ## Versions - [ca476cb923a8](https://savrn.com/models/modernbert-large-nli/versions/ca476cb923a8) · 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 2026-10-21. - [Hugging Face record](https://huggingface.co/tasksource/ModernBERT-large-nli) - [How the hub is built](https://savrn.com/model-hub/methodology)