Instructions to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Use Docker

docker model run hf.co/khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

LM Studio
Jan
Ollama
How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with Ollama:
```
ollama run hf.co/khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
```
Unsloth Desktop

How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with Docker Model Runner:
```
docker model run hf.co/khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16
```

Lemonade

How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-4B-Agentic-Coding-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use khazarai/Qwen3.5-4B-Agentic-Coding-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "khazarai/Qwen3.5-4B-Agentic-Coding-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.5-4B-Agentic-Coding

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

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.