⚡ ColBERT-ko-v1.0 Complete On-device Study: SOTA Korean Retrieval on Mobile
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⚡ ColBERT-ko-v1.0 Complete On-device Study: SOTA Korean Retrieval on Mobile
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Major Release: Comprehensive mobile deployment study of yoonjong's ColBERT-ko-v1.0 with detailed performance benchmarks across 50+ mobile devices!
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Major Release: Comprehensive mobile deployment study of yoonjong's ColBERT-ko-v1.0 with detailed performance benchmarks across 50+ mobile devices!
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🎯 Model Overview:
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🎯 Model Overview:
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Architecture: Korean-optimized ColBERT (late interaction)
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Architecture: Korean-optimized ColBERT (late interaction)
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Parameters: 0.1B (compact and efficient)
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Parameters: 0.1B (compact and efficient)
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Specialty: Korean document retrieval and semantic search
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Specialty: Korean document retrieval and semantic search
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Performance: 1.0 recall@10, 0.966 nDCG@10 on AutoRAGRetrieval
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Performance: 1.0 recall@10, 0.966 nDCG@10 on AutoRAGRetrieval
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Benchmark: Outperforms Jina-ColBERT-v2 on Korean MTEB tasks
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Benchmark: Outperforms Jina-ColBERT-v2 on Korean MTEB tasks
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📊 Mobile Performance Results:
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📊 Mobile Performance Results:
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Latency Metrics:
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Latency Metrics:
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NPU (Best): 3.17ms average inference
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NPU (Best): 3.17ms average inference
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GPU: 11.67ms average
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GPU: 11.67ms average
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CPU: 21.36ms average
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CPU: 21.36ms average
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NPU Advantage: 18.46x speedup over CPU
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NPU Advantage: 18.46x speedup over CPU
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Memory Efficiency:
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Memory Efficiency:
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Model Size: 567.89 MB (production optimized)
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Model Size: 567.89 MB (production optimized)
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Runtime Memory: 170.87 MB peak consumption
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Runtime Memory: 170.87 MB peak consumption
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Load Range: 4-614 MB across device categories
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Load Range: 4-614 MB across device categories
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FP32 Memory: 642.65 MB (optional high precision)
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FP32 Memory: 642.65 MB (optional high precision)
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Accuracy Preservation:
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Accuracy Preservation:
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- FP16 Precision: 53.51 dB maintained
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- FP16 Precision: 53.51 dB maintained
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- Quantized Mode: 32.77 dB (available for memory constraints)
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- Quantized Mode: 32.77 dB (available for memory constraints)
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-Retrieval Quality: Production-grade Korean semantic matching
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-Retrieval Quality: Production-grade Korean semantic matching
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🔬 Research Implications:
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🔬 Research Implications:
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This study demonstrates:
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This study demonstrates:
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- Late interaction models are viable for mobile deployment
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- Late interaction models are viable for mobile deployment
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- Korean language models achieve SOTA performance on consumer hardware
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- Korean language models achieve SOTA performance on consumer hardware
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- Sub-5ms retrieval enables real-time RAG applications
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- Sub-5ms retrieval enables real-time RAG applications
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- Privacy-first Korean AI is now technically feasible
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- Privacy-first Korean AI is now technically feasible
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Benchmark Methodology:
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Benchmark Methodology:
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- Standardized Korean query inputs
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- Standardized Korean query inputs
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- Multiple inference cycles with statistical averaging
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- Multiple inference cycles with statistical averaging
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- Real-world Korean document corpus testing
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- Real-world Korean document corpus testing
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 - Thermal and battery impact assessment
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 - Thermal and battery impact assessment
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Deployment Recommendations:
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Deployment Recommendations:
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- Use NPU acceleration for 18x speedup on Android
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- Use NPU acceleration for 18x speedup on Android
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- Implement MUVERA for real-time requirements
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- Implement MUVERA for real-time requirements
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- Cache frequent queries for instant responses
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- Cache frequent queries for instant responses
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- Progressive loading for large document collections
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- Progressive loading for large document collections
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- Hybrid approach for extremely large corpora (local + cloud)
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- Hybrid approach for extremely large corpora (local + cloud)
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🔗 Resources:
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🔗 Resources:
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Complete Study: 
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Complete Study: 
https://mlange.zetic.ai/p/Steve/colbert_kor
https://mlange.zetic.ai/p/Steve/colbert_kor
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Original Model: 
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Original Model: 
https://huggingface.co/yoonjong0505/ColBERT-ko-v1.0
https://huggingface.co/yoonjong0505/ColBERT-ko-v1.0
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⚡ ColBERT-ko-v1.0 Complete On-device Study: SOTA Korean Retrieval on Mobile
Major Release: Comprehensive mobile deployment study of yoonjong's ColBERT-ko-v1.0 with detailed performance benchmarks across 50+ mobile devices!

🎯 Model Overview:
Architecture: Korean-optimized ColBERT (late interaction)
Parameters: 0.1B (compact and efficient)
Specialty: Korean document retrieval and semantic search
Performance: 1.0 recall@10, 0.966 nDCG@10 on AutoRAGRetrieval
Benchmark: Outperforms Jina-ColBERT-v2 on Korean MTEB tasks

📊 Mobile Performance Results:

Latency Metrics:
NPU (Best): 3.17ms average inference
GPU: 11.67ms average
CPU: 21.36ms average
NPU Advantage: 18.46x speedup over CPU

Memory Efficiency:
Model Size: 567.89 MB (production optimized)
Runtime Memory: 170.87 MB peak consumption
Load Range: 4-614 MB across device categories
FP32 Memory: 642.65 MB (optional high precision)

Accuracy Preservation:
- FP16 Precision: 53.51 dB maintained
- Quantized Mode: 32.77 dB (available for memory constraints)
-Retrieval Quality: Production-grade Korean semantic matching

🔬 Research Implications:

This study demonstrates:
- Late interaction models are viable for mobile deployment
- Korean language models achieve SOTA performance on consumer hardware
- Sub-5ms retrieval enables real-time RAG applications
- Privacy-first Korean AI is now technically feasible

Benchmark Methodology:
- Standardized Korean query inputs
- Multiple inference cycles with statistical averaging
- Real-world Korean document corpus testing
 - Thermal and battery impact assessment

Deployment Recommendations:
- Use NPU acceleration for 18x speedup on Android
- Implement MUVERA for real-time requirements
- Cache frequent queries for instant responses
- Progressive loading for large document collections
- Hybrid approach for extremely large corpora (local + cloud)

🔗 Resources:
Complete Study: https://mlange.zetic.ai/p/Steve/colbert_kor
Original Model: https://huggingface.co/yoonjong0505/ColBERT-ko-v1.0

🤗
yeonseok-zeticai ilyoung
🚀
yeonseok-zeticai John6666
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yeonseok-zeticai
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yeonseok-zeticai
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yeonseok-zeticai