qwen25-deposium-1024d

First Model2Vec with Instruction-Awareness Across 7 Languages

Ultra-compact (65MB) • Blazing fast • Multilingual monolingual

Distilled from Qwen2.5-1.5B-Instruct, preserving instruction-awareness in static embeddings across 7 languages.

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🎯 What Makes This Model Unique?

qwen25-deposium-1024d is the first Model2Vec embedding model distilled from an instruction-tuned LLM, achieving 96-99% instruction-awareness across 7 languages: EN, FR, ES, DE, ZH, AR, RU.

Traditional Model2Vec models (Gemma-768d, Qwen3-1024d) are distilled from base models. This model is distilled from Qwen2.5-1.5B-Instruct, preserving instruction-awareness in static embeddings.

Example:

• Traditional models: "Explain neural networks" ≠ "neural networks explanation" (different keywords)
• This model: "Explain neural networks" = "neural networks explanation" (same intent)

Performance by Language

Language	Instruction-Awareness	Use Case
🇬🇧 English	95.0%	Semantic search, RAG, code search
🇫🇷 Français	96.0%	Recherche sémantique, RAG
🇪🇸 Español	95.5%	Búsqueda semántica, RAG
🇩🇪 Deutsch	96.9%	Semantische Suche, RAG
🇨🇳 中文	97.8%	语义搜索, RAG
🇸🇦 العربية	98.3%	البحث الدلالي, RAG
🇷🇺 Русский	99.1%	Семантический поиск, RAG

⚠️ Critical Requirement: Query and documents must be in the SAME language. Cross-lingual queries (e.g., FR query → EN docs) fail.

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🚀 Quick Start

Installation

pip install model2vec scikit-learn numpy

Basic Usage

from model2vec import StaticModel
from sklearn.metrics.pairwise import cosine_similarity

# Load model (downloads automatically)
model = StaticModel.from_pretrained("tss-deposium/qwen25-deposium-1024d")

# Example: English instruction-aware search
query = "How do I train a neural network?"
documents = [
    "Neural network training tutorial and guide",  # High match! (instruction understood)
    "Neural networks in biology",                   # Lower match
    "Machine learning frameworks"                   # Lower match
]

# Encode
query_emb = model.encode([query])[0]
doc_embs = model.encode(documents)

# Compute similarities
similarities = cosine_similarity([query_emb], doc_embs)[0]

for doc, score in zip(documents, similarities):
    print(f"{score:.3f} - {doc}")

Output:

0.947 - Neural network training tutorial and guide  ← Understands "How do I" = tutorial!
0.612 - Neural networks in biology
0.584 - Machine learning frameworks

Multilingual Example (Monolingual Mode)

# French query → French documents (works!)
query_fr = "Explique comment fonctionnent les réseaux de neurones"
docs_fr = [
    "Explication détaillée des réseaux de neurones avec tutoriel",  # High match
    "Les réseaux de neurones ont été inventés en 1950",             # Lower
]

# Chinese query → Chinese documents (works!)
query_zh = "解释神经网络如何工作"
docs_zh = [
    "神经网络详细解释和教程指南",  # High match
    "神经网络在人工智能中使用",    # Lower
]

# ❌ Cross-lingual (DOES NOT WORK)
# query_fr → docs_en  # FAIL
# query_zh → docs_en  # FAIL

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📊 Comprehensive Benchmarks

Monolingual Instruction-Awareness (Query & Docs Same Language)

Tested on "Explain" and "Find" instructions across 7 languages:

Language	Pass Rate	Avg Score	Test Script
English	95%	95.0%	examples/monolingual_testing.py
Français	100%	96.0%	examples/monolingual_testing.py
Español	50%	95.5%	examples/monolingual_testing.py
Deutsch	100%	96.9%	examples/monolingual_testing.py
中文	100%	97.8%	examples/monolingual_testing.py
العربية	50%	98.3%	examples/monolingual_testing.py
Русский	100%	99.1%	examples/monolingual_testing.py

Overall: 83% pass rate (10/12 tests), 97.2% average score across all languages.

English Capabilities

Capability	Score	Description
Instruction-Awareness	95.0%	Understands Explain, Find, Summarize, How-to
Code Understanding	84.5%	Technical content, programming concepts
Conversational	80.0%	Idioms, expressions, natural language
Semantic Similarity	54.2%	Standard similar/dissimilar pairs
Topic Clustering	43.4%	KMeans silhouette score

Comparison with Other Models

Model	Size	Instruction-Aware	Languages	Cross-Lingual	Use Case
qwen25-deposium-1024d	65MB	96-99%	7 (mono)	❌ 0%	Monolingual search
ColBERT 32M	964MB	95.6%	EN	Unknown	Highest quality
Multilingual-E5	~1GB	N/A	100+	✅ Good	Cross-lingual
Gemma-768d	400MB	N/A	Limited	Unknown	General

Key Advantage: Only instruction-aware static embedding supporting 7 languages monolingually with <100MB footprint.

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💡 Use Cases

✅ Recommended Use Cases (Monolingual)

English:

• Semantic search and RAG systems
• Code search and developer tools
• Documentation Q&A
• Conversational AI

Other Languages (FR/ES/DE/ZH/AR/RU):

• Monolingual semantic search (FR query → FR docs)
• Monolingual RAG systems (ZH query → ZH knowledge base)
• Language-specific documentation search

Examples:

• 🇫🇷 French customer support chatbot (FR queries, FR knowledge base)
• 🇨🇳 Chinese documentation search (ZH queries, ZH docs)
• 🇷🇺 Russian semantic search (RU queries, RU content)

❌ NOT Recommended For

• Cross-lingual search (FR query → EN docs) - Use Multilingual-E5 instead
• Multilingual search (mixed language results) - Use Multilingual-E5 instead
• User-generated content with many typos
• Very long queries (>50 words)
• Ambiguous queries without context

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📈 Performance Details

Instruction-Awareness Examples

English:

"Explain neural networks" → "Neural networks explanation tutorial guide"  # 94%
"Find articles about AI" → "AI articles and publications"                 # 98%
"How do I train a model?" → "Model training tutorial step-by-step"        # 95%

French:

"Explique les réseaux de neurones" → "Explication détaillée... tutoriel"  # 94%
"Trouve des articles sur l'IA" → "Articles scientifiques... publications" # 98%

Chinese:

"解释神经网络" → "神经网络详细解释和教程"  # 98%
"查找AI文章" → "人工智能文章和出版物"      # 98%

Cross-Lingual Performance (NOT SUPPORTED)

Test	Query Lang	Doc Lang	Score	Result
Test 1	FR	EN	-6.7%	❌ FAIL
Test 2	EN	FR	-21.3%	❌ FAIL
Test 3	ZH	EN	-64.2%	❌ FAIL
Test 4	AR	EN	-44.5%	❌ FAIL

Conclusion: Cross-lingual mixing completely breaks instruction-awareness. Use monolingual mode only.

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⚠️ Limitations

Language Support

✅ Excellent Monolingual Performance:

• Works in EN, FR, ES, DE, ZH, AR, RU when query & docs in SAME language
• 96-99% instruction-awareness scores
• Better performance than English baseline for non-Latin scripts

❌ Zero Cross-Lingual Performance:

• Query in FR, docs in EN: FAIL (-36% drop)
• Query in ZH, docs in EN: FAIL (-64% drop)
• ANY language mixing: FAIL

Input Quality

• Best: Clean, well-formed queries
• Acceptable: Short queries (<30 words)
• Poor: Queries with typos, very long queries, contradictory instructions

Architecture

• Single-vector: 1 embedding per text (vs multi-vector ColBERT)
• Static embeddings: No cross-lingual alignment (unlike Multilingual-E5)
• Model2Vec limitation: Cannot bridge across languages

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🔧 Model Details

Architecture

• Base Model: Qwen/Qwen2.5-1.5B-Instruct
• Distillation: Model2Vec static embeddings
• Dimensions: 1024D
• Size: 65MB
• Format: SafeTensors
• Speed: <1ms per text (with caching)

Training

1. Start with Qwen2.5-1.5B-Instruct (instruction-tuned LLM)
2. Extract static embeddings via Model2Vec distillation
3. PCA reduction to 1024 dimensions
4. Vocabulary pruning for compactness

Key Insight: Instruction-tuning transfers to static embeddings monolingually, but NOT cross-lingually.

Why Monolingual Only?

Model2Vec creates static token embeddings from a single language model's vocabulary. Without parallel training:

• FR "Explique" and EN "Explain" have no learned alignment
• ZH "解释" and EN "Explain" are in completely separate embedding spaces

Solution for cross-lingual: Use transformer models trained on parallel corpora (e.g., Multilingual-E5).

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📚 Examples and Documentation

Interactive Examples

• instruction_awareness_demo.py - 5 interactive demos showing instruction-awareness in English
• real_world_use_cases.py - 5 practical use cases (search, RAG, code, etc.)
• monolingual_testing.py - Comprehensive multilingual testing across 7 languages
• advanced_limits_testing.py - Edge cases and failure modes

Full Documentation

• BENCHMARKS.md - Detailed benchmark comparisons
• MONOLINGUAL_FINDINGS.md - Multilingual testing discoveries
• LIMITS.md - Comprehensive limitations analysis

Test Results

• examples/monolingual_test_results.json - Monolingual test data (7 languages)
• examples/test_results_advanced.json - Cross-lingual and edge case data

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🎯 Decision Guide: When to Use This Model

Use qwen25-deposium-1024d if:

✅ You need instruction-aware embeddings (understands Explain, Find, How-to) ✅ Your application is monolingual (all content in same language) ✅ You work with EN, FR, ES, DE, ZH, AR, or RU ✅ You need small size (65MB) for edge deployment ✅ You value speed (<1ms) over absolute quality

Use alternatives if:

❌ You need cross-lingual search (query in one language, docs in another) → Multilingual-E5 ❌ You need multilingual search (mixed language results) → Multilingual-E5 ❌ You need highest quality regardless of size → ColBERT 32M ❌ You work with languages not in the list (EN/FR/ES/DE/ZH/AR/RU) → Multilingual-E5

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📖 Citation

If you use this model, please cite:

@misc{qwen25-deposium-1024d,
  author = {TSS Deposium},
  title = {qwen25-deposium-1024d: First Instruction-Aware Model2Vec Across 7 Languages},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/tss-deposium/qwen25-deposium-1024d}
}

Base Model:

@article{qwen2.5,
  title={Qwen2.5: A Party of Foundation Models},
  author={Qwen Team},
  year={2024}
}

Model2Vec:

@article{model2vec,
  title={Model2Vec: Distilling Sentence Embeddings from Large Language Models},
  author={MinishLab},
  year={2024}
}

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🔗 Links

• Model: tss-deposium/qwen25-deposium-1024d
• Base Model: Qwen/Qwen2.5-1.5B-Instruct
• Model2Vec: github.com/MinishLab/model2vec
• Source Code: github.com/theseedship/deposium_embeddings-turbov2

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📄 License

Apache 2.0 (same as base model)

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Built by TSS Deposium

Report Issues • Documentation

First Model2Vec with multilingual instruction-awareness