Different Teachers, Different Capabilities: Sub-1B On-Device Distillation for Structured Text Enrichment
arXiv:2607.08268v1 Announce Type: cross Abstract: High-volume structured extraction pays a large model's latency on every item, so distilling the task into a small on-device model is attractive: comparable output at a fraction of the time and cost. We measure what that distillation actually delivers, per sub-task. Each news article is mapped to one JSON object with a short summary and five categorical labels. We distill an 8B reasoning teacher (deepseek-r1:8b) into a 0.6B student (Qwen3-0.6B; QLoRA, three seeds), and add two teacher controls: a same-size non-reasoning teacher and a larger mana
The proliferation of powerful large language models necessitates efficient on-device deployment solutions, making distillation techniques crucial for practical applications.
This research demonstrates a viable path for deploying sophisticated AI capabilities on resource-constrained devices, lowering operational costs and latency for high-volume tasks.
The ability to run near-large-model performance for specific tasks on smaller, local models significantly expands the potential applications and accessibility of AI.
- · Edge AI hardware manufacturers
- · Companies with high-volume structured data extraction needs
- · Developers targeting mobile and embedded systems
- · Cloud providers offering distillation services
- · Companies reliant solely on large, centralized inference models
- · Legacy on-premise data processing solutions
Wider adoption of AI for real-time, on-device data processing due to reduced cost and latency.
Increased demand for specialized small language models and efficient distillation techniques across various industries.
Enhanced data privacy as more processing occurs locally, potentially sparking new regulations or ethical considerations around on-device AI capabilities.
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