khazarai

Qwen3.5-4B-Agentic-Coding

README

License: apache-2.0

Model Summary

Qwen3.5-4B-Agentic-Coding is a fine-tuned, 4-billion parameter language model optimized for agentic coding tasks, structured technical reasoning, and automated tool interaction. Built upon the Qwen3.5 architecture, this model excels at multi-step problem solving, refactoring codebases without logic gaps, and seamlessly executing tool calls (Bash, Python, File Editor, Browser).

Unlike general-purpose models that may produce loose code syntax or lose track of historical code patterns during refactoring, Qwen3.5-4B-Agentic-Coding maintains a strict internal chain of thought to preserve execution correctness (e.g., accurately demonstrating legacy Callback-to-Async transformations).

Intended Use

Primary Use Cases

Agentic Coding Assistants: Autonomous software development agents using external tool execution.
Code Refactoring & Optimization: Transforming legacy paradigms (e.g., callback hell) into modern asynchronous or modular paradigms with strict logical checks.
Structured Technical Reasoning: Explaining architectural decisions and step-by-step logic before generating executable output.
Interactive Tool Execution: Generating structured commands for environments supporting Bash, Python, or file operations.

Out-of-Scope / Non-Recommended Use

Non-English programming inquiries or multi-lingual conversational tasks.
Direct deployment in production without human-in-the-loop validation for critical code infrastructure.

Training Dataset & Tools

The model was fine-tuned on a high-quality dataset designed to boost agentic problem-solving capabilities.

Integrated Dataset Tools

Table with columns: Tool, Purpose
Tool	Purpose
`editor`	File creation, updating, and repository-wide modifications.
`bash`	Executing terminal commands and managing execution pipelines.
`python`	In-line code validation, sandboxed testing, and mathematical checks.
`browser`	Real-time technical documentation lookup and validation.

Strengths & Behavioral Characteristics

Precise Problem Framing: Validates legacy structures before refactoring, preventing logical contradictions (such as confusing Promises with Callbacks).
Minimal Injected Friction: Keeps core implementations clean and standalone without adding unnecessary, non-functional code abstractions.
Structured System Thought: Embeds step-by-step reasoning blocks (<think>) prior to code delivery for complete transparency.

Limitations & Biases

Synthetically Generated: The fine-tuning dataset relies on synthetic distillation techniques, which may occasionally echo systemic patterns from the teacher models.
Language Restriction: Optimizations are strictly centered around English code annotations and prompt instructions.
Model Biases: Inherits standard biases from the core Qwen3.5 architecture.othai/unsloth)

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Model Details

Model Provider

khazarai

Model Tree

Base

unsloth/Qwen3.5-4B

Fine-tuned

this model

Input Modalities

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Output Modalities

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Supported Functionality