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embedme/lightonai-denseon-Q8_0
lightonai-denseon-Q8_0 is a sentence similarity model from embedme. Use it when you need a score for how close two texts are. It is set up for litembeddings. The card lists the license as apache-2.0.
A Q80 GGUF conversion of lightonai/DenseOn, a 149M-parameter ModernBERT dense retrieval model. DenseOn emits one 768-dimensional, L2-normalized vector using CLS pooling and was trained with asymmetric query/document p…
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.gguf160 MB · 100%
From the Hugging Face model README
A Q8_0 GGUF conversion of lightonai/DenseOn, a 149M-parameter ModernBERT dense retrieval model. DenseOn emits one 768-dimensional, L2-normalized vector using CLS pooling and was trained with asymmetric query/document prefixes.
| File | Purpose | SHA-256 |
|---|---|---|
DenseOn-Q8_0.gguf | Q8_0 ModernBERT encoder with native CLS pooling metadata | 506d5bab02a4adf7c00a71fef2bb58bfa54f769e15670886e212af7c16b4ccff |
.load ./litembeddings
SELECT lembed_model(
'/path/to/DenseOn-Q8_0.gguf',
json_object('ctx_size', 512, 'batch_size', 512)
);
-- Prefixes are required by DenseOn's training contract.
SELECT lembed('query: best database indexing strategy');
SELECT lembed('document: covering indexes can avoid table lookups');
Use query: for queries and document: for corpus passages. Omitting or swapping these prefixes can reduce retrieval quality. The source model's maximum sequence length is 512 tokens.
lightonai/DenseOncb9947ebccb33862d24e3c7ca2edb25e51acd8876eddde06a4f25d55d538b5d15628dcc2b6882147An end-to-end parity check against the source FP32 Transformers pipeline produced cosine similarity 0.999389 for the validation query. Small differences are expected from Q8_0 quantization.
The source model is released under Apache 2.0. See the DenseOn model card for training details, evaluation results, intended use, limitations, and citation information. This repository is an independent conversion and is not affiliated with LightOn.