schneewolflabs/Wichtelchen-Qwen3.5-9B
Wichtelchen-Qwen3.5-9B by schneewolflabs is a 9 billion parameter language model based on the Qwen3.5 architecture, designed for local operation and specialized in delegating engineering tasks to a code agent. It achieves 56.1% on hembench for Hemlock code generation, demonstrating near-parity with its 27B parameter sibling in agent delegation capabilities. This model is optimized for tool use and code agent interaction, making it suitable for automated engineering workflows.
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Model Overview
Wichtelchen-Qwen3.5-9B is a 9 billion parameter model developed by schneewolflabs, built upon the Qwen3.5-9B-Base architecture. It is a smaller, more efficient version of the Wichtel-Qwen3.6-27B, specifically fine-tuned for local operation and delegating engineering work to a code agent. The model demonstrates strong performance in code generation, achieving 56.1% on hembench for Hemlock code, which is close to the 27B model's 67.6% at a significantly reduced parameter count.
Key Capabilities
- Code Agent Delegation: Excels at handing off engineering tasks to a code agent, achieving 10/10 on the delegate axis of the egirl 47-case benchmark.
- Hemlock Code Generation: Proficient in generating Hemlock code, scoring 56.1% on the hembench weighted metric.
- Tool Use: Incorporates Hemlock tool use and is designed for single-turn tool interactions, bound to the egirl tool schema.
- Behavioral Consistency: Maintains general capabilities, with ARC scores and wiki perplexity remaining stable across its training rungs, indicating focused behavioral improvements without sacrificing core language understanding.
Limitations
- Call Formatting: May occasionally emit malformed tool calls, requiring a lenient parser or additional preference training.
- Restraint: Can sometimes over-delegate or reach for commands like
git_statusorexecute_commandwhen a direct answer is expected. - Hemlock stdlib: Similar to other models in its family, it has a weaker grasp of standard library function names, necessitating documentation checks.
Use Cases
This model is particularly well-suited for applications requiring automated code generation, agent-based engineering task delegation, and scenarios where efficient local operation with strong tool-use capabilities is paramount.