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QuerynAi/queryn-adapter-fastembed-bge-small_to_qwen3-emb-8b
queryn-adapter-fastembed-bge-small_to_qwen3-emb-8b is a feature extraction model from QuerynAi. Use it when you need embeddings to search or compare text. The card lists the license as mit.
Translates an embedding produced by fastembed-bge-small into the embedding space of qwen3-emb-8b, so a corpus already embedded with fastembed-bge-small can be served against a qwen3-emb-8b index without re-embedding i…
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.onnx6.3 MB · 95%
From the Hugging Face model README
fastembed-bge-small → qwen3-emb-8bTranslates an embedding produced by fastembed-bge-small into the embedding space of qwen3-emb-8b, so a corpus already embedded with fastembed-bge-small can be served against a qwen3-emb-8b index without re-embedding it. Part of the Queryn embedding-translation engine.
| Source model | fastembed-bge-small (384-d) |
| Target model | qwen3-emb-8b (4096-d) |
| Architecture | linear (plain linear projection) |
| Parameters | ~1.6M |
| Best test cosine similarity | 0.7007 (epoch 15) |
| ONNX opset | 17 |
Architecture ablation (best test cosine): linear 0.7007 ← saved, deep 0.6972.
source_embedding — float32, shape [batch, 384]. Raw fastembed-bge-small embeddings; the graph L2-normalizes them itself, so pre-normalization is neither required nor harmful.target_embedding — float32, shape [batch, 4096], unit-normalized, in qwen3-emb-8b space.import numpy as np, onnxruntime as ort
from huggingface_hub import hf_hub_download
path = hf_hub_download("QuerynAi/queryn-adapter-fastembed-bge-small_to_qwen3-emb-8b", "model.onnx")
sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
src = np.random.rand(4, 384).astype(np.float32) # your fastembed-bge-small embeddings
tgt = sess.run(["target_embedding"], {"source_embedding": src})[0]
assert tgt.shape == (4, 4096) # unit vectors in qwen3-emb-8b space
Trained on paired embeddings over a unified multi-domain corpus — arXiv abstracts, Australian case law, SQuAD passages, PubMed abstracts, and crypto/markets news (~350k rows spanning science, legal, QA, medical, and finance). Loss: 1 - mean cosine similarity, Adam, ReduceLROnPlateau, best-epoch checkpoint. Both a linear baseline and the MLP are trained for every pair; the higher-scoring one is published (ties go to linear).

fastembed-bge-small → all targets: learning curves and best scores (this pair included).

Linear vs. deep for every pair (black ring = saved architecture).
Full adapter set: Queryn Embedding Adapters
models/v1/fastembed-bge-small_to_qwen3-emb-8b.pt (sha256 ed50d7edc0209b90…)ptConverter.pyReleased under the MIT license.