# nomic-embed-text-v1.5 by Nomic AI: Open-Weight Model
Source: https://savrn.com/models/nomic-embed-text-v1-5
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Runs On

What it takes to serve nomic-embed-text-v1.5 (137M 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 22, 2026.

[nomic-embed-text-v1.5 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/nomic-embed-text-v1-5/gpus)

## SAVRN's Notes on nomic-embed-text-v1.5

137M parameters, a 2,048-token context, 0.3 GB at 16-bit, 0.1 GB at 4-bit: this sentence embedder is a passenger on whatever card you already run. The files are the interesting part — nine weight builds including ONNX fp16, int8, uint8, bnb4, q4 and q4f16 alongside model.safetensors — so CPU and edge deployments are first-class rather than afterthought conversions. The cheapest setup we price is one 192 GB MI300X at $1.85 an hour.

Apache 2.0 permits commercial use with notices kept. Two operational details from the card: every input string needs a task instruction prefix — search_document: for corpus text, search_query: for queries — and the model is built on Matryoshka Representation Learning, so you can resize output dimensions to trade recall for footprint. Loaders older than transformers 5.5.0 and sentence-transformers 5.3.0 need trust_remote_code. A companion vision model shares the embedding space, so text and images can index together.

SAVRN Research

## Model Card

By Nomic AI, published under apache-2.0, revision e9b6763023c6.

### nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning

[Blog](https://www.nomic.ai/blog/posts/nomic-embed-text-v1) | [Technical Report](https://savrn.com/papers/nomic-embed-training-a-reproducible-long-context-text-embedder) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Nomic Platform](https://atlas.nomic.ai)

Exciting Update!: nomic-embed-text-v1.5 is now multimodal! [nomic-embed-vision-v1.5](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) is aligned to the embedding space of nomic-embed-text-v1.5, meaning any text embedding is multimodal!

### Usage

Important: the text prompt must include a task instruction prefix, instructing the model which task is being performed.

For example, if you are implementing a RAG application, you embed your documents as search_document: <text here> and embed your user queries as search_query: <text here>.

Notice: From transformers v5.5.0 and sentence transformers v5.3.0, trust_remote_code=True will no longer be necessary. This will only be possible with the text-only series as of now.

### Task instruction prefixes

#### search_document

##### Purpose: embed texts as documents from a dataset

This prefix is used for embedding texts as documents, for example as documents for a RAG index.

[Read the full model card (884 words)](https://savrn.com/models/nomic-embed-text-v1-5/card)

## Configuration

Architecture

NomicBertModel

Context length (tokens)

2,048

Layers

12

Hidden size

768

Feed-forward size

3,072

Attention heads

12

Head dimension

64

Vocabulary size

30,528

Stored precision

float32

Model type

nomic_bert

## Identity and Version

Repository

nomic-ai/nomic-embed-text-v1.5

Publisher

Nomic AI

Task

Sentence similarity

Modality

Text

Library

sentence-transformers

Parameters

137M parameters

Languages

en

Revision

e9b6763023c676ca8431644204f50c2b100d9aab

First published

2024-02-10

Last updated

2026-04-07

## Files and Weights

20 files, 2.2 GB in total. The weights are 9 files totalling 2.2 GB in onnx, safetensors.

Weights9 files · 2.2 GB

Configuration6 files · 4.0 KB

Tokenizer3 files · 944.1 KB

Documentation1 file · 71.8 KB

Repository1 file · 1.5 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model.safetensors | Weights | 546.9 MB | 9e7d262b1fe5 |
| onnx/model.onnx | Weights | 547.3 MB | 147d5aa88c21 |
| onnx/model_bnb4.onnx | Weights | 158.0 MB | eb81cef4642a |
| onnx/model_fp16.onnx | Weights | 273.9 MB | cf5b5a86edb0 |
| onnx/model_int8.onnx | Weights | 137.3 MB | b4342336deba |
| onnx/model_q4.onnx | Weights | 165.1 MB | 314976b7b9fb |
| onnx/model_q4f16.onnx | Weights | 111.1 MB | 3dbab6709d86 |
| onnx/model_quantized.onnx | Weights | 137.3 MB | b4342336deba |
| onnx/model_uint8.onnx | Weights | 137.3 MB | f7ee635388c9 |
| 1_Pooling/config.json | Configuration | 286 B | — |
| config.json | Configuration | 2.5 KB | — |
| config_sentence_transformers.json | Configuration | 140 B | — |
| modules.json | Configuration | 255 B | — |
| sentence_bert_config.json | Configuration | 58 B | — |
| special_tokens_map.json | Configuration | 695 B | — |
| README.md | Documentation | 71.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.4 KB | — |
| tokenizer_config.json | Tokenizer | 1.2 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

2.2 GB

[Download from Nomic AI](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)

Released by Nomic AI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Described by [arXiv:2205.13147](https://savrn.com/papers/matryoshka-representation-learning)
- Described by [arXiv:2402.01613](https://savrn.com/papers/nomic-embed-training-a-reproducible-long-context-text-embedder)

## 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 |
| --- | --- | --- | --- | --- | --- |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 75.209 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric apComparison conditions not established | 38.5761 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 69.3559 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric accuracyComparison conditions not established | 91.8144 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric apComparison conditions not established | 88.6522 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric f1Comparison conditions not established | 91.8043 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 47.162 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 46.5933 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1Comparison conditions not established | 24.253 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_10Comparison conditions not established | 38.962 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_100Comparison conditions not established | 40.081 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1000Comparison conditions not established | 40.089 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_3Comparison conditions not established | 33.499 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_5Comparison conditions not established | 36.351 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1Comparison conditions not established | 24.609 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_10Comparison conditions not established | 39.099 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_100Comparison conditions not established | 40.211 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1000Comparison conditions not established | 40.219 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_3Comparison conditions not established | 33.677 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_5Comparison conditions not established | 36.469 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1Comparison conditions not established | 24.253 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_10Comparison conditions not established | 48.011 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_100Comparison conditions not established | 52.756 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1000Comparison conditions not established | 52.965 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_3Comparison conditions not established | 36.564 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_5Comparison conditions not established | 41.712 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1Comparison conditions not established | 24.253 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_10Comparison conditions not established | 7.738 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_100Comparison conditions not established | 0.98 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1000Comparison conditions not established | 0.1 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_3Comparison conditions not established | 15.149 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_5Comparison conditions not established | 11.593 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1Comparison conditions not established | 24.253 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_10Comparison conditions not established | 77.383 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_100Comparison conditions not established | 98.009 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1000Comparison conditions not established | 99.644 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_3Comparison conditions not established | 45.448 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_5Comparison conditions not established | 57.966 | [nomic-ai](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) Publisher reported | Evaluated revision not stated | — |
| mteb/arguana | Task ArguAnaMetric ArguAnaSetup Obtained using MTEB v1.18.0Comparison conditions not established | 52.018 | [Obtained using MTEB v1.18.0](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-05 |
| mteb/arguana | Task ArguAna_default_testMetric ArguAna_default_testSetup Obtained using MTEB v1.18.0Comparison conditions not established | 52.018 | [Obtained using MTEB v1.18.0](https://github.com/embeddings-benchmark/mteb/) Reported by a third party | Evaluated revision not stated | 2026-03-05 |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 2.2 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.

## Compare nomic-embed-text-v1.5

- [distiluse-base-multilingual-cased-v1 vs nomic-embed-text-v1.5](https://savrn.com/models/compare/distiluse-base-multilingual-cased-v1-vs-nomic-embed-text-v1-5)
- [distiluse-base-multilingual-cased-v2 vs nomic-embed-text-v1.5](https://savrn.com/models/compare/distiluse-base-multilingual-cased-v2-vs-nomic-embed-text-v1-5)
- [multilingual-e5-small vs nomic-embed-text-v1.5](https://savrn.com/models/compare/multilingual-e5-small-vs-nomic-embed-text-v1-5)

## Questions About nomic-embed-text-v1.5

### How much GPU memory does nomic-embed-text-v1.5 need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (137M parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run nomic-embed-text-v1.5 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 nomic-embed-text-v1.5 commercially?

Yes. nomic-embed-text-v1.5 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 nomic-embed-text-v1.5's context length?

2,048 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Sentence similarity

### [nomic-embed-text-v1](https://savrn.com/models/nomic-embed-text-v1)

[Nomic AI](https://savrn.com/model-publishers/nomic-ai)

nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. Exciting Update!: nomic-embed-text-v1 is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of nomic-embed-text-v1, meaning any text embedding is multimodal! Important: the text prompt must include a task instruction prefix, instructing the model which task is being performed. For example, if you are implementing a RAG application, you embed your documents as searchdocument: and embed your user queries as searchquery:. Notice: From transformers v5.5.0 and sentence transformers v5.3.0…

Open weights apache-2.0 137M parameters 8,192 tokens sentence-transformers

[View model](https://savrn.com/models/nomic-embed-text-v1)

Model · Sentence similarity

### [issue-loc-embed-small](https://savrn.com/models/issue-loc-embed-small)

[Chao Ma](https://savrn.com/model-publishers/ickma2311)

A 137M bi-encoder that ranks the functions of a repository by how likely they need to change to resolve a GitHub issue. It is nomic-ai/CodeRankEmbed (MIT) fine-tuned with an independent re-implementation of the SweRank data recipe (arXiv 2505.07849) on permissively licensed repositories only. No SweRank code or weights were used for training, filtering or mining. Licence: Apache-2.0. v0.2 (this revision, tag v0.2) was trained on Python and on Go, TypeScript, JavaScript, Rust, Java, PHP, C and C++ issues. v0.1 (Python only, the first release) stays available under the tag v0.1. The model's remote code (from CodeRankEmbed) reads the weights of the default branch even when revision= is given…

Open weights apache-2.0 137M parameters sentence-transformers

[View model](https://savrn.com/models/issue-loc-embed-small)

Model · Sentence similarity

### [distiluse-base-multilingual-cased-v1](https://savrn.com/models/distiluse-base-multilingual-cased-v1)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Open weights apache-2.0 135M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/distiluse-base-multilingual-cased-v1)

Model · Sentence similarity

### [distiluse-base-multilingual-cased-v2](https://savrn.com/models/distiluse-base-multilingual-cased-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Open weights apache-2.0 135M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/distiluse-base-multilingual-cased-v2)

Model · Sentence similarity

### [KielEmbed-Code](https://savrn.com/models/kielembed-code)

[Tech](https://savrn.com/model-publishers/kiel2)

This is a sentence-transformers model finetuned from microsoft/codebert-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - numtrainepochs: 1 - learningrate: 2e-05 - warmupsteps: 0.1 - gradientaccumulationsteps: 4 - fp16: True - perdevicetrainbatchsize: 8 - numtrainepochs: 1 - maxsteps: -1 - learningrate: 2e-05 - lrschedulertype: linear - lrschedulerkwargs: None - warmupsteps: 0.1 - optim…

Open weights 125M parameters 514 tokens sentence-transformers

[View model](https://savrn.com/models/kielembed-code)

Model · Sentence similarity

### [lateon-msmarco-hardneg-n10573-e1-ckpt82-qd1024](https://savrn.com/models/lateon-msmarco-hardneg-n10573-e1-ckpt82-qd1024)

[Dangerousmanleebyeonggeon](https://savrn.com/model-publishers/dangerousmanleebyeonggeon)

This is a PyLate model finetuned from lightonai/LateOn. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search. Load the ColBERT model and initialize the PLAID index, then encode and index your documents: Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: Once the documents are indexed, you can retrieve the top-k most relevant documents for a…

Open weights 149M parameters 8,192 tokens PyLate

[View model](https://savrn.com/models/lateon-msmarco-hardneg-n10573-e1-ckpt82-qd1024)

## Nomic AI

[All models and datasets](https://savrn.com/model-publishers/nomic-ai)

## Versions

- [e9b6763023c6](https://savrn.com/models/nomic-embed-text-v1-5/versions/e9b6763023c6) · current 2026-10-22

## Explore More

- [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity)
- [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 2026-10-19.
- [Nomic AI website](https://www.nomic.ai/)
- [Hugging Face record](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
- [How the hub is built](https://savrn.com/model-hub/methodology)