vsr-embeddings-v1-text-small

vSR Embeddings v1 Text Small

Router-first multilingual embeddings for retrieval, semantic matching, clustering, and signal-driven model routing.

Xunzhuo/vsr-embeddings-v1-text-small is the small member of the vSR text embedding family. It is built for the request path of an AI gateway: predictable task prompts, adjustable embedding width, efficient long-document matching, and a clean interface to parallel safety and routing signals.

Why it is different

Router-native task contract

One semantic model serves retrieval, text matching, clustering, and generic classification embeddings. Query and document roles use explicit task instructions, so applications do not need separate ad-hoc prompt wrappers.

Matryoshka as a latency dial

The same model supports 1024, 512, 256, 128, 64 dimensions. A deployment can use a smaller vector for hot-path routing and a wider vector for high-accuracy search without changing the model family or index semantics.

Multi-vector long-document retrieval

Long inputs are represented by fixed 320-token chunks with 64-token overlap. Documents are scored with query-to-chunk max-sim, which preserves local evidence that a single pooled vector can dilute. The runtime contract covers documents up to 32K tokens while keeping each model forward short and batchable.

Signal-compatible by design

The embedding model runs in parallel with vSR's domain, safety, fact-check, feedback, modality, and PII extractors. The decision layer keeps those signals separate and inspectable, while this model supplies the shared semantic geometry. That preserves explainability and lets the backend catalog scale independently.

Two deployment tiers

Quality

MI300X serving profile

The profile uses BF16 vLLM pooling on one AMD Instinct MI300X. Throughput is a capacity reference, not a guarantee for every server configuration.

Standard embeddings

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Xunzhuo/vsr-embeddings-v1-text-small", trust_remote_code=True)
vectors = model.encode(
    ["Instruct: Retrieve semantically similar text\nQuery: route this request"],
    normalize_embeddings=True,
)

vSR runtime

import sys
from huggingface_hub import snapshot_download

repo = snapshot_download("Xunzhuo/vsr-embeddings-v1-text-small")
sys.path.insert(0, repo)
from vsr_runtime import VSRTextEmbeddingModel

model = VSRTextEmbeddingModel(repo)

query = model.encode(
    ["Find a model that understands this request"],
    task="retrieval",
    role="query",
    dimension=1024,
)

scores = model.score_long_documents(
    "multilingual routing evidence",
    ["A long candidate document ..."],
)

Deployment guidance

• Use text-small for always-on routing, semantic cache lookup, and high-QPS retrieval.
• Use text-large when multilingual accuracy and difficult semantic matching dominate latency.
• Direct encoding defaults to 4096 tokens. Use the included max-sim runtime for long documents instead of treating 32K text as one vector.
• This is a bi-encoder embedding model, not a cross-encoder reranker and not a sequence-level safety classifier.

Foundation and license

v1 preserves the foundation geometry of microsoft/harrier-oss-v1-0.6b at the revision recorded in vsr_family_manifest.json. vSR adds the router-native task, representation, and serving contract described above. See THIRD_PARTY_NOTICES.md for concise attribution. License: mit.