Use and license follow the base model. See the base repository for terms, citation, and safety documentation.
Original model README (reference)
The content below is copied from the base repository README.md for convenience. Only the YAML front matter at the very top of this file applies to this model card; the sections below are reference only.
Update:
This model has been further enhanced with additional reasoning data distilled from Qwen3.5-27B.
The new training data introduces higher-quality reasoning trajectories across domains such as science, instruction-following, and mathematics.
Part of the data comes from Jackrong/Qwen3.5-reasoning-700x, a curated dataset designed to improve structured step-by-step reasoning and reasoning diversity.
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💡 Model Introduction
Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled is a highly capable reasoning model fine-tuned on top of the Qwen3.5-9B dense architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions.
Through Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted <think> tags, and ultimately delivering precise, nuanced solutions.
🗺️ Training Pipeline Overview
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1Base Model (Qwen3.5-9B)
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4Supervised Fine-Tuning (SFT) + LoRA
5(Response-Only Training masked on "<|im_start|>assistant\n<think>")
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8Final Model Text-only (Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled)
🧠 Example of Learned Reasoning Scaffold（Example）
The model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern: “Let me analyze this request carefully: 1..2..3...”.
This streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.
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1Let me analyze this request carefully:
231. Identify the core objective of the problem.
42. Break the task into clearly defined subcomponents.
53. Evaluate constraints and edge cases.
64. Formulate a step-by-step solution plan.
75. Execute the reasoning sequentially and verify consistency.
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🔹 Supervised Fine-Tuning (SFT)
Objective: To inject high-density reasoning logic and establish a strict format for problem-solving involving an internal thinking state prior to outputting the final response.
Method: We utilized Unsloth for highly efficient memory and compute optimization. A critical component of this stage is the train_on_responses_only strategy, masking instructions so the loss is purely calculated over the generation of the <think> sequences and the subsequent solutions.
Format Enforcement: All training samples were systematically normalized so the model strictly abides by the structure <think> {internal reasoning} </think>\n {final answer}.
📈 Training Loss Curve
The training loss showed a strong and healthy downward trend throughout the run, demonstrating effective knowledge distillation. Starting from an initial loss of 0.5138, the model converged steadily to a final loss of 0.35786 — indicating the model successfully internalized the structured <think> reasoning patterns from the Claude 4.6 Opus teacher data.
📚 All Datasets Used
The dataset consists of high-quality, filtered reasoning distillation data:
Additional curated reasoning samples designed to strengthen structured step-by-step problem solving and improve reasoning diversity.
🌟 Core Skills & Capabilities
Modular & Structured Thinking: Inheriting traits from Opus-level reasoning, the model demonstrates confident parsing of the prompt, establishing an outlined plan in its <think> block sequentially rather than exploratory "trial-and-error" self-doubt.
Extended Context Support: Fine-tuned smoothly with a 16,384 token context window allowing complex multi-step reasoning traces to exist gracefully within memory limits.
⚠️ Limitations & Intended Use
Hallucination Risk: While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.
Intended Scenario: Best suited for offline analytical tasks, coding, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic.
🙏 Acknowledgements
Significant thanks to the Unsloth AI team for making rapid fine-tuning of large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets (nohurry and TeichAI).
