QuerynAi/queryn-adapter-fastembed-bge-small_to_nemotron-1b-free
Queryn adapter — fastembed-bge-small → nemotron-1b-free
Translates an embedding produced by fastembed-bge-small into the embedding space of nemotron-1b-free, so a corpus already embedded with fastembed-bge-small can be served against a nemotron-1b-free index without re-embedding it. Part of the Queryn embedding-translation engine.
Specs
Architecture ablation (best test cosine): linear 0.5519, deep 0.5529 ← saved.
Input / output contract
- Input
source_embedding— float32, shape[batch, 384]. Rawfastembed-bge-smallembeddings; the graph L2-normalizes them itself, so pre-normalization is neither required nor harmful. - Output
target_embedding— float32, shape[batch, 2048], unit-normalized, innemotron-1b-freespace. - Batch axis is dynamic.
Usage
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_nemotron-1b-free", "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, 2048) # unit vectors in nemotron-1b-free spaceTraining
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).
Plots
`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
Provenance
- Source checkpoint:
models/v1/fastembed-bge-small_to_nemotron-1b-free.pt(sha256bcb2ec8d02564343…) - Converted: 2026-08-30T20:45:22+00:00 · torch 2.13.0 ·
ptConverter.py
License
Released under the MIT license.
