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Dl26/
Veyra-Embed-300M

Name: Veyra-Embed-300M
Availability: InStock

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Veyra-Embed-300M: Sentence Similarity Embedding Model

Veyra-Embed-300M is a from-scratch sentence embedding model developed by Dl26. It maps text into normalized dense vectors for sentence similarity, semantic search, clustering, and retrieval-style experiments.

The model is trained with a contrastive objective over real sentence pairs using in-batch negatives. It is designed as a compact encoder-style embedding model rather than a generative language model.

Model Details

Property	Value
Developer	Dl26
Model type	Sentence embedding encoder
Parameters	305,113,088
Hidden size	1024
Layers	4
Tokenizer vocab size	32,430
Model vocab size	260,000
Embedding size	1024
Max length	64
Objective	Symmetric contrastive InfoNCE
Training data	sentence-transformers/all-nli, sentence-transformers/stsb, sentence-transformers/quora-duplicates, embedding-data/QQP_triplets

Usage

This checkpoint contains raw PyTorch/safetensors weights plus tokenizer files. A compatible implementation should create the same encoder architecture from config.json, load model.safetensors, then mean-pool and normalize the output embedding.

python

from safetensors.torch import load_file

state_dict = load_file("model.safetensors")

Intended Use

sentence similarity
semantic search
duplicate question retrieval
clustering
lightweight embedding research
ranking experiments

Limitations

It is not a generative language model.
It should be evaluated on target retrieval and similarity datasets before use.
It will not match large production embedding models trained on much larger curated mixtures.

Dl26/Veyra-Embed-300M