OsakanaTeishoku/Qwen3-4B-Thinking-2507-reasoning-ja-20260329

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Mar 29, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

OsakanaTeishoku/Qwen3-4B-Thinking-2507-reasoning-ja-20260329 is a 4 billion parameter Qwen3-based causal language model developed by OsakanaTeishoku. Fine-tuned on the DataPilot/Knowledge-QA-SingleTurn-Dataset, this model is specifically designed to generate Japanese reasoning responses to Japanese inputs. It features a context length of 16384 tokens and is optimized for knowledge-based question answering in Japanese.

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Model Overview

OsakanaTeishoku/Qwen3-4B-Thinking-2507-reasoning-ja-20260329 is a 4 billion parameter language model developed by OsakanaTeishoku. It is based on the Qwen3 architecture and was fine-tuned from unsloth/Qwen3-4B-Thinking-2507 using Unsloth and Huggingface's TRL library, resulting in 2x faster training.

Key Capabilities

  • Japanese Reasoning: The model is specifically trained to generate detailed reasoning in Japanese for Japanese inputs.
  • Knowledge-based QA: It was fine-tuned using the DataPilot/Knowledge-QA-SingleTurn-Dataset, making it suitable for single-turn knowledge question-answering tasks.
  • Context Length: Supports a context length of 16384 tokens, allowing for processing of moderately long inputs.

Intended Use Cases

This model is ideal for applications requiring:

  • Generating explanatory or reasoning-based responses in Japanese.
  • Answering factual questions in Japanese based on provided knowledge.
  • Integrating into systems that need a compact yet capable Japanese-centric reasoning model.