mirror-memory-default-embedding-model download | Hugging Bay

Q: What is this model?

KeZZ08/mirror-memory-default-embedding-model is an embedding model for text embeddings published by KeZZ08, added to Hugging Bay Sep 30, 2026. Model from Hugging Face for feature extraction: KeZZ08/mirror-memory-default-embedding-model · sentence transformers

Q: Can I use it commercially?

It is released under the mit license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

Hosted here: 7 files, 134.2 MB. Full repo upstream: 134.2 MB. The recommended download, model.safetensors, is 133.5 MB.

Q: What can I download here?

Hosted here: 7 files, 134.2 MB. Full repo upstream: 134.2 MB. The original release is at https://huggingface.co/KeZZ08/mirror-memory-default-embedding-model.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 7 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-kezz08-mirror-memory-default-embedding-model/safety on Hugging Bay.

Q: Will KeZZ08/mirror-memory-default-embedding-model run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.

KeZZ08

mirror-memory-default-embedding-model

KeZZ08/mirror-memory-default-embedding-model

Model details

text embeddingsLicense: mitSHA-256 recorded for 7 of 11 filessafetensors

Content controls: not assessed.

The catalog lists this model for turning text into vectors. Catalog sources: license, cardMetadata.license, baseModel, tags, task, cardMetadata.pipelineTag.

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Your next step

Use it from code

This Python snippet downloads the selected file. Running the model needs its own library and setup.

from urllib.request import urlretrieve

urlretrieve(
    "https://huggingbay.xyz/api/downloads/hf-model-kezz08-mirror-memory-default-embedding-model/model.safetensors",
    "model.safetensors",
)

Other ways

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7 hosted of 11 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors133 MB · 1 file Download · 133 MB (1 file)

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Compare your file’s SHA-256 with the recorded hash.

7 hosted of 11 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	133.5 MB	`496c70d4…dff6ce97` `496c70d4083e4317c622fdad74ec3271b620dd0100d88baea20c8180dff6ce97`	Hosted	Télécharger
Tokenizer & config
`1_Pooling/config.json`	89 bytes	—	Upstream only	File metadata
`2_Normalize/config.json`	97 bytes	—	Upstream only	File metadata
`config.json`	824 bytes	`4019ef85…8b16351e` `4019ef8577daa2bd8ee858637e16c68eb9e5d6f7069c7b58af513e108b16351e`	Hosted	Télécharger
`config_sentence_transformers.json`	295 bytes	`f7e3fd51…344d173f` `f7e3fd51b5855b2ec821a58c8f5dae53d1009bce34189ecbd04012d6344d173f`	Hosted	Télécharger
`modules.json`	432 bytes	`5861cc45…df0f5d0e` `5861cc45c276c15a212ad0a21a5a8664bb8ee0f70b1857d92a7f3e94df0f5d0e`	Hosted	Télécharger
`sentence_bert_config.json`	250 bytes	`5fee3fcd…42eb4c19` `5fee3fcdc4a727fad422f0fed0c867b0921eefa9051de221521956ed42eb4c19`	Hosted	Télécharger
`tokenizer.json`	711.5 KB	`fdc154bb…b78c306a` `fdc154bbab2030fc5268e7e765203efd3253e38ac176aca0ddf87c0ab78c306a`	Hosted	Télécharger
`tokenizer_config.json`	461 bytes	`101aeeb5…03b3120a` `101aeeb501e32e26fe79b342409f6a74d7e366862b41ac979bfb650303b3120a`	Hosted	Télécharger
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	11.4 KB	—	Upstream only	File metadata

Vérifier un téléchargement

In the folder where you saved model.safetensors, run:

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shasum -a 256 -- 'model.safetensors'

Linux

sha256sum -- 'model.safetensors'

Windows

Get-FileHash -LiteralPath 'model.safetensors' -Algorithm SHA256

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mirror-memory-default-embedding-model

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