Nomic embed text v1.5
7900°
nomic-ai | embedding model | Apache-2.0 license | 2.1 GB as a torrent. Long-context text embeddings with Matryoshka dims. Full mirror of nomic-ai/nomic-embed-text-v1.5. **Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! nomic-embed-vision-v1.5 is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal!
Guides for this model
Best local LLM by use case (October 2026): the right open model for every job
What is The Hugging Bay? A mirror index for open models, and how to verify a download with its hashes
Open weights vs open source vs API-only: what you are actually allowed to do
How to Install Ollama and Run a Local AI (Windows, Mac, Linux)
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
Nomic-embed-text-v1.5 is a long-context text embedding model from Nomic AI, released under Apache-2.0 and available as a 2.1 GB torrent. Its headline feature is Matryoshka representation learning, which lets you truncate embedding dimensions without retraining. The companion vision model shares the same embedding space, so text and image embeddings are directly comparable.
Known for
- Matryoshka dims: shrink embedding size at inference without retraining.
- Long-context support for text embeddings, beyond typical short-passage limits.
- Shared embedding space with nomic-embed-vision-v1.5 enables cross-modal search.
- Apache-2.0 licence: commercial use, modification and redistribution all permitted.
Watch out for
- Open weights are not on Hugging Face directly; you get the model via torrent mirror.
- This is an embedding model, not a generative one — it produces vectors, not text.
Best for
Prompts to try
Semantic search ranking
I have a corpus of customer support tickets. Explain how I would use nomic-embed-text-v1.5 to build a semantic search system over them: how to embed the documents, how to embed a query, and how to rank results by cosine similarity. Include a short Python sketch using the sentence-transformers library.
Matryoshka dims trade-off
I want to store embeddings from nomic-embed-text-v1.5 but my vector database has a storage budget. Walk me through how Matryoshka representation learning works, what dimension sizes are reasonable to try, and how I should evaluate the quality loss when I truncate from the full dimension down to a smaller one.
Cross-modal retrieval design
I want to let users search a product catalogue by typing a description and retrieve matching product images. Explain how the shared embedding space between nomic-embed-text-v1.5 and nomic-embed-vision-v1.5 makes this possible, what I need to index, and how a query at runtime would work.
Compare with
- DeepSeek V4.1 FlashOpen-weights model1,048,576 token context · $0.04/M input, $0.68/M output
- GLM 5.3 FlashOpen-weights model1,048,576 token context · $0.02/M input, $0.72/M output
- Hy4 previewOpen-weights model1,048,576 token context · $0.83/M input, $2.50/M output
- MiMo-V2.6-FlashOpen-weights model1,050,000 token context · $0.14/M input, $0.28/M output
More text models from nomic-ai
Questions people ask
- Can I run nomic-embed-text-v1.5 locally?
- Yes. The weights are available under Apache-2.0 as a 2.1 GB torrent, so you can download and run them on your own hardware with no usage fees.
- What are Matryoshka embeddings and why do they matter?
- Matryoshka representation learning trains the model so that the first N dimensions of an embedding are already meaningful on their own, letting you trade storage and speed against retrieval quality by simply truncating the vector without retraining.
- Is this a chat or generative model?
- No — nomic-embed-text-v1.5 produces dense vector embeddings for use in semantic search, retrieval-augmented generation pipelines and similarity tasks; it does not generate text.
Captain's verdictA permissively licensed, locally runnable embedding model with flexible dimensions and a built-in path to multimodal retrieval — worth a look for any RAG pipeline.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
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Updated Oct 22, 2026
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Also ranked: #75 Hottest Text / LLM text model
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nomic embed text v1.5 Popularity Trend
Heat score, last 12 months
Heat score
7,900°
This week
n/a
Wasn't released/tracked
This month
n/a
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Past year
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7,000°
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+900°
Torrent seeders
1 people keep serving the weights over torrents or hosted mirrors. Serving a file costs real bandwidth and says the weights are still wanted, so each seeder counts as 1,000 downloads in the download term.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 7,900°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.1,000 download-equivalents in all, from Torrent seeders. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+900°7,900°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.7,900°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Run Locally
How to install
Best local LLM by use case (October 2026): the right open model for every job
Oct 13, 2026
Torrents
nomic-ai/nomic-embed-text-v1.5
onnx/model.onnx
model.safetensors
onnx/model_fp16.onnx
onnx/model_q4.onnx
onnx/model_bnb4.onnx
onnx/model_uint8.onnx
onnx/model_int8.onnx
onnx/model_quantized.onnx
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Nomic embed text v1.5 is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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