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qwen3-student-qwen3_compressed_distilled
yrrhall/qwen3-student-qwen3_compressed_distilled
qwen3-student-qwen3_compressed_distilled is a sentence similarity model from yrrhall. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as apache-2.0.
Compact multilingual sentence encoder compressed from Qwen/Qwen3-0.6B (48x compression).
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At a glance
- Task
- Sentence Similarity
- Library
- sentence-transformers
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Jul 17, 2026
- Updated
- Jul 17, 2026
- SHA
- d1cefd62
Model card
From the Hugging Face model README
qwen3_compressed_distilled (Distilled)
Compact multilingual sentence encoder compressed from Qwen/Qwen3-0.6B (48x compression).
Model Details
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-0.6B |
| Architecture | qwen3 (decoder) |
| Hidden dim | 448 (from 1024) |
| Layers | 4 (from 28) |
| Intermediate | 1344 |
| Attention heads | 7 |
| KV heads | 1 |
| Vocab size | 7,341 (from 151,936) |
| Parameters | ~12.4M |
| Model size (FP32) | 47.1MB |
| Compression | 48x |
| Distilled | Yes (2-stage) |
Quick Start
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("qwen3_compressed_distilled", trust_remote_code=True)
sentences = [
"Hello, how are you?",
"안녕하세요, 잘 지내세요?",
"こんにちは、元気ですか？",
"你好，你好吗？",
]
embeddings = model.encode(sentences)
print(embeddings.shape) # (4, 448)
MTEB Evaluation Results
Overall Average: 32.29%
| Task Group | Average |
|---|---|
| Classification | 39.54% |
| Clustering | 29.66% |
| STS | 27.68% |
Classification
| Task | Average | Details |
|---|---|---|
| AmazonCounterfactualClassification | 57.59% | en: 61.01%, en-ext: 57.63%, de: 57.34%, ja: 54.4% |
| Banking77Classification | 33.06% | default: 33.06% |
| ImdbClassification | 51.77% | default: 51.77% |
| MTOPDomainClassification | 40.28% | es: 49.82%, en: 49.41%, fr: 41.34%, th: 38.1%, de: 36.01% |
| MassiveIntentClassification | 18.75% | zh-CN: 39.84%, en: 32.94%, ko: 31.09%, ja: 30.64%, fr: 28.88% |
| MassiveScenarioClassification | 22.01% | zh-CN: 53.77%, en: 42.48%, ja: 41.01%, ko: 40.22%, es: 36.93% |
| ToxicConversationsClassification | 51.26% | default: 51.26% |
| TweetSentimentExtractionClassification | 41.62% | default: 41.62% |
Clustering
| Task | Average | Details |
|---|---|---|
| ArXivHierarchicalClusteringP2P | 48.97% | default: 48.97% |
| ArXivHierarchicalClusteringS2S | 49.66% | default: 49.66% |
| BiorxivClusteringP2P.v2 | 7.42% | default: 7.42% |
| MedrxivClusteringP2P.v2 | 19.2% | default: 19.2% |
| MedrxivClusteringS2S.v2 | 20.79% | default: 20.79% |
| StackExchangeClustering.v2 | 45.32% | default: 45.32% |
| StackExchangeClusteringP2P.v2 | 33.99% | default: 33.99% |
| TwentyNewsgroupsClustering.v2 | 11.97% | default: 11.97% |
STS
| Task | Average | Details |
|---|---|---|
| BIOSSES | 26.5% | default: 26.5% |
| SICK-R | 36.86% | default: 36.86% |
| STS12 | 37.37% | default: 37.37% |
| STS13 | 22.61% | default: 22.61% |
| STS14 | 19.72% | default: 19.72% |
| STS15 | 37.43% | default: 37.43% |
| STS17 | 17.77% | en-en: 37.23%, ko-ko: 33.84%, es-es: 32.55%, ar-ar: 28.19%, fr-en: 14.73% |
| STSBenchmark | 23.14% | default: 23.14% |
Distillation Impact
| Task | Before | After | Delta |
|---|---|---|---|
| AmazonCounterfactualClassification | 53.9% | 57.59% | +3.69%p |
| ArXivHierarchicalClusteringP2P | 46.3% | 48.97% | +2.67%p |
| ArXivHierarchicalClusteringS2S | 46.53% | 49.66% | +3.13%p |
| BIOSSES | 1.1% | 26.5% | +25.4%p |
| Banking77Classification | 9.18% | 33.06% | +23.88%p |
| BiorxivClusteringP2P.v2 | 5.86% | 7.42% | +1.56%p |
| ImdbClassification | 50.25% | 51.77% | +1.52%p |
| MTOPDomainClassification | 28.9% | 40.28% | +11.38%p |
| MassiveIntentClassification | 15.91% | 18.75% | +2.84%p |
| MassiveScenarioClassification | 17.94% | 22.01% | +4.07%p |
| MedrxivClusteringP2P.v2 | 17.63% | 19.2% | +1.57%p |
| MedrxivClusteringS2S.v2 | 18.42% | 20.79% | +2.37%p |
| SICK-R | 22.32% | 36.86% | +14.54%p |
| STS12 | 7.97% | 37.37% | +29.4%p |
| STS13 | 18.76% | 22.61% | +3.85%p |
| STS14 | 10.4% | 19.72% | +9.32%p |
| STS15 | 24.3% | 37.43% | +13.13%p |
| STS17 | 10.58% | 17.77% | +7.19%p |
| STSBenchmark | 7.96% | 23.14% | +15.18%p |
| StackExchangeClustering.v2 | 40.91% | 45.32% | +4.41%p |
| StackExchangeClusteringP2P.v2 | 30.96% | 33.99% | +3.03%p |
| ToxicConversationsClassification | 50.35% | 51.26% | +0.91%p |
| TweetSentimentExtractionClassification | 34.47% | 41.62% | +7.15%p |
| TwentyNewsgroupsClustering.v2 | 9.52% | 11.97% | +2.45%p |
Training
Stage 1: Model Compression
- Teacher:
Qwen/Qwen3-0.6B(28L, 1024d, 596M params) - Compression: Layer pruning → Hidden dim reduction → Vocab pruning
- Result: 4L / 448d / 7,341 vocab
Stage 2: Two-Stage Knowledge Distillation
Compression ratio 48x requires progressive distillation:
- Stage 1: Teacher (596M) → Intermediate (~119M)
- MSE + Cosine Similarity loss
- MTEB task datasets (Classification/Clustering/STS)
- Stage 2: Intermediate → Final Student (12.4M)
- Same training objective
- AdamW (lr=2e-5, weight_decay=0.01), Cosine annealing
Supported Languages (18)
ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl
- Task
- Sentence Similarity
- Library
- sentence-transformers
- Type
- qwen3
- License
- apache-2.0
- Languages
- ko, en, ja, zh, es, fr
- Created
- Jul 17, 2026
- Updated
- Jul 17, 2026