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

🤗 Hugging Face sourceimage-text-to-textapache-2.027 GBGGUF✓ HF checksums availableupdated today
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Qwen3.5-9B-Kimi-k3-Distilled

Model Summary

Qwen3.5-9B-Kimi-k3-Distilled is a fine-tuned, reasoning-focused model derived from the Qwen architecture via knowledge distillation from Kimi K3 (moonshotai/kimi-k3).

Unlike standard instruction-tuned models that heavily rely on speculative internal chain-of-thought hallucination, this model is explicitly optimized for environment-grounded agentic workflows. It natively integrates reasoning with tool execution, prioritizing direct environment interaction (e.g., executing shell commands, listing files, reading logs) over ungrounded hypothetical reasoning loops.

Key Features & Behavior

  • Grounded Tool Calling First: Prefers inspecting files, running test scripts, and querying environments over guessing code structure or hallucinating codebase state.
  • Behaviour-Preserving Distillation: Inherits the multi-step trajectory planning, tool selection, and state tracking behaviors of Kimi K3.
  • Loop Resilience: Specialized in build-test-fix loops and verification-driven task completion (e.g., SWE-bench style debugging, Bash/CLI automation).
  • Parallel & Structured Tool Calls: Supports structured tool schemas and parallel tool execution when required by the agent harness.

Intended Use

This model is designed primarily for:

  1. Supervised Fine-Tuning (SFT) & Distillation Baselines: Serves as a strong base or control model for agentic datasets.
  2. Coding & Shell Automation Agents: Executing bash commands, repository exploration, bug fixing, and continuous integration troubleshooting.
  3. Multi-Step Agentic Trajectories: Analysis and deployment of tasks requiring long-horizon planning, parallel calls, tool selection, state tracking, and self-verification.

Out-of-Scope / Misuse

  • Direct deployment without sandbox isolation (the model is trained to execute shell commands and code).
  • General ungrounded long-form creative writing where strict environment feedback is absent.

Training Details

Dataset & Trajectory Source

The dataset consists of behaviour-preserving instruction-following, tool-use, and agent trajectories distilled from Kimi K3 (moonshotai/kimi-k3).

Programming Languages Covered

The training trajectory drop includes multi-language code contexts, scripts, and tool calls across: Python, TypeScript, English, Go, Bash, C, C#, Java, C++, Ruby, Assembly, PowerShell, Rust, and general technical plain text (unknown).

Task Distribution

The training trajectory mix emphasizes structural tool integration rather than static QA:

  • Environment exploration and file inspection (ls, grep, cat, repo navigation).
  • Iterative error handling and test-driven fixes.
  • Multi-tool chaining and tool argument serialization.

Benchmark & Behavioral Comparison

During internal evaluations comparing Qwen3.5-9B against Qwen3.5-9B-Kimi-k3-Distilled on agentic/coding benchmarks:

Model Grounded Tool Call Rate Speculative Hallucination Rate Task Completion Efficiency
Qwen3.5-9B (Base) Low (tends to guess) High (long hypothetical text chains) Sub-optimal in agent loops
Qwen3.5-9B-Kimi-k3-Distilled High (Immediate Execution) Low (Action-driven reasoning) High (Fast convergence)

Example Trajectory Difference

  • Task: Debug a broken deployment script (release.sh) in a workspace.
  • Base Model (Qwen3.5-9B): Generates long speculative reasoning loops trying to guess missing code and hypothetical argument-passing bugs without checking the actual directory.
  • Distilled Model (Qwen3.5-9B-Kimi-k3-Distilled): Immediately triggers a bash tool call (ls -la / cat release.sh) to inspect the real file before drawing conclusions.