Adapting Methods for Domain-Specific Japanese Small LMs: Scale, Architecture, and Quantization

• URL: http://arxiv.org/abs/2603.18037v1
• Date: Thu, 12 Mar 2026 18:27:57 GMT
• Title: Adapting Methods for Domain-Specific Japanese Small LMs: Scale, Architecture, and Quantization
• Abstract summary: This paper presents a systematic methodology for building domain-specific Japanese small language models using QLoRA fine-tuning.<n>We address three core questions: optimal training scale, base-model selection, and architecture-aware quantization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a systematic methodology for building domain-specific Japanese small language models using QLoRA fine-tuning. We address three core questions: optimal training scale, base-model selection, and architecture-aware quantization. Stage 1 (Training scale): Scale-learning experiments (1k--5k samples) identify n=4,000 as optimal, where test-set NLL reaches minimum (1.127) before overfitting at 5k samples. Stage 2 (Compare finetuned SLMs): Comparing four Japanese LLMs shows that Llama-3 models with Japanese continual pre-training (Swallow-8B, ELYZA-JP-8B) outperform multilingual models (Qwen2.5-7B). Stage 3 (Quantization): Llama-3 architectures improve under Q4_K_M quantization, while GQA architectures degrade severely (Qwen2.5: -0.280 points). Production recommendation: Swallow-8B Q4_K_M achieves 2.830/3 score, 8.9 s/question, 4.9 GB size. The methodology generalizes to low-resource technical domains and provides actionable guidance for compact Japanese specialist LMs on consumer hardware.

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