Jackrong/Qwopus3-5-9B-Coder-Flash
Jackrong/Qwopus3-5-9B-Coder-Flash is a 9 billion parameter, 32K context length coding and agent-focused fine-tune of Qwen3.5-9B. Developed by Jackrong, this model is optimized for efficient, focused reasoning to produce useful code and clear actions, significantly reducing unproductive generation overhead. It excels in local code generation, debugging, and tool-assisted development, demonstrating improved coding quality and reasoning efficiency on benchmarks like HumanEval+ and BenchLocal.
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Qwopus3.5-9B-Coder-Flash: Efficient Coding and Agent Model
Qwopus3.5-9B-Coder-Flash is a 9 billion parameter model, fine-tuned from Qwen3.5-9B, specifically designed for coding and agent-focused tasks. It emphasizes an "efficiency-first" philosophy, aiming to provide practical problem-solving capabilities on local hardware by reducing unproductive reasoning and focusing on generating useful code and clear actions. This model is built upon the Unsloth distribution and retains the original chat template and MTP weights.
Key Capabilities
- Optimized for Coding and Agents: Fine-tuned to excel in code generation, debugging, and tool-assisted development, particularly for local agent tasks.
- Enhanced Reasoning Efficiency: Achieves a 78.87% reduction in reasoning tokens compared to its base model (Qwen3.5-9B) on HumanEval+, leading to 5.18x greater reasoning efficiency (correct samples per 1,000 reasoning tokens).
- Improved Coding Quality: Demonstrates a 9.56% relative increase in correct samples and a 7.68 percentage-point gain in Pass@1 on HumanEval+ v0.1.10.
- Strong Agent Performance: Achieved higher overall scores on BenchLocal for local agent task evaluation, indicating better performance across various agent capabilities.
- Reduced Reasoning Variance: Significantly decreased reasoning-length variance by over 85% overall, leading to more consistent and focused outputs.
Good For
- Local Code Generation: Ideal for developers needing efficient code generation and debugging on local machines.
- Agent Development: Suitable for experimenting with and deploying compact, efficient agents that require focused reasoning and clear action outputs.
- Tool-Assisted Development: Effective in scenarios where the model needs to interact with tools and produce actionable code.
- Resource-Constrained Environments: Designed to make useful work accessible on local hardware, aligning with an efficiency-first approach.