khazarai/Qwen3.5-9B-Kimi-k3-Distilled

Model Overview

Qwen3.5-9B-Kimi-k3-Distilled is a 9 billion parameter model based on the Qwen architecture, created through knowledge distillation from moonshotai/kimi-k3. Unlike models that rely on speculative internal chain-of-thought, this model is specifically designed for environment-grounded agentic workflows. It integrates reasoning with tool execution, focusing on direct interaction with the environment, such as executing shell commands or inspecting files.

Key Capabilities

• Grounded Tool Calling: Prioritizes inspecting environments (e.g., files, logs) over guessing or hallucinating states.
• Behavior-Preserving Distillation: Inherits multi-step trajectory planning, tool selection, and state tracking from Kimi K3.
• Loop Resilience: Specialized in build-test-fix loops and verification-driven task completion, ideal for debugging and CLI automation.
• Parallel & Structured Tool Calls: Supports structured tool schemas and parallel execution for agent harnesses.

Intended Use Cases

• Supervised Fine-Tuning (SFT) & Distillation Baselines: Serves as a robust base for agentic datasets.
• Coding & Shell Automation Agents: Effective for executing bash commands, repository exploration, bug fixing, and CI troubleshooting.
• Multi-Step Agentic Trajectories: Suitable for tasks requiring long-horizon planning, parallel calls, tool selection, state tracking, and self-verification across multiple programming languages including Python, TypeScript, Go, Bash, C, C#, Java, C++, Ruby, Assembly, PowerShell, and Rust.

Behavioral Comparison

Internal evaluations show this distilled model has a high grounded tool call rate and low speculative hallucination rate compared to the base Qwen3.5-9B, leading to high task completion efficiency in agent loops. For instance, when debugging, the distilled model immediately uses tool calls (e.g., ls -la, cat) to inspect files, whereas the base model generates lengthy speculative reasoning.