Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2 is a 9 billion parameter Qwen3.5-based language model fine-tuned by Jackrong, specifically optimized for efficient chain-of-thought reasoning. This second iteration (v2) was trained with 14,000 Claude 4.6 Opus-style general reasoning samples, focusing on concise, reusable reasoning patterns. It excels at tasks requiring logical deduction, mathematics, and general problem-solving, demonstrating improved reasoning speed and accuracy while reducing token consumption. The model is particularly suited for resource-constrained deployments, agentic workflows, and applications where reasoning economy is critical.
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Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
This model is the second iteration of a 9 billion parameter Qwen3.5 fine-tune by Jackrong, engineered to significantly enhance the efficiency and accuracy of chain-of-thought reasoning. It was trained using 14,000 Claude 4.6 Opus-style general reasoning samples, with a focus on distilling concise and reusable reasoning patterns rather than just maximizing raw benchmark scores.
Key Capabilities & Differentiators
- Efficient Reasoning: Drastically improves reasoning speed and reduces token consumption (over 20% fewer characters/tokens) while increasing absolute accuracy.
- General Reasoning Scaffold: Primarily trained on general-domain reasoning data (mathematics, word problems, logical deduction, general knowledge), leading to a robust and transferable reasoning logic.
- Cross-Task Generalization: Achieves high performance on HumanEval and HumanEval+ benchmarks, demonstrating strong generalization despite not being code-centric in training.
- Optimized for Economy: Explicitly designed to trim overhead from verbose reasoning, making it ideal for scenarios where reasoning efficiency per unit of inference budget is crucial.
Good For
- Resource-constrained local deployment: Reduces latency and memory pressure on consumer GPUs or lower-memory setups.
- Agentic workflows: Improves throughput and lowers cumulative inference costs in multi-step agent systems.
- Open-source tool use and agent stacks: Highly practical for lightweight reasoning systems and autonomous agent projects.
- Offline analytical tasks, coding, math, and heavy logic-dependent prompting: Provides transparent internal logic for users to follow.