Instructions to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1
# Run inference directly in the terminal:
./build/bin/llama cli -hf khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Use Docker

docker model run hf.co/khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Llama new
LM Studio
Jan

vLLM

How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Ollama
How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1
```
Unsloth Desktop

How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1
```

Lemonade

How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Run and chat with the model

lemonade run user.Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF-Q4_1

List all available models

lemonade list

Hermes Agent

How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1

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/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF:Q4_1" \
  --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"

Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF : GGUF

Benchmark: khazarai/Multi-Domain-Reasoning-Benchmark
Total Questions: 100

Model	Score
khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled	75.64
Qwen/Qwen3-4B-Thinking-2507	73.73

This is a reasoning-distilled variant of Qwen3-4B-Thinking, fine-tuned using LoRA via Unsloth to replicate the advanced reasoning capabilities of the larger Qwen3.6-plus teacher model. The distillation process focuses on reducing the "rambling" and "uncertainty" often found in smaller models during complex tasks, replacing them with concise, structured, and actionable solution paths.

Reasoning Comparison: Base vs. Distilled

The primary improvement in this model is the qualitative leap in reasoning structure. Below is a summary of the differences observed when solving complex graph problems (e.g., Shortest Path with Edge Reversals):

Base Model (Qwen3-4B-Thinking):

Style: Stream-of-consciousness, exploratory, and verbose.
Behavior: The model often talks to itself ("Hmm, interesting", "Wait, no"), struggles to interpret problem constraints correctly on the first try, and enters loops of self-correction. It mimics a student trying to figure out the problem as they speak.
Output: Contains high noise-to-signal ratio; solution paths are often buried under paragraphs of hesitation.

Distilled Model (Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled):

Style: Structured, professional, and report-oriented.
Behavior: The model analyzes the problem immediately, separates concerns (Input, Output, Constraints), and formulates a concrete algorithm plan (e.g., State-Space Dijkstra). It proceeds with confidence, avoiding logical dead-ends.
Output: Provides a clean breakdown: Problem Analysis -> Intuition -> Algorithm -> Complexity Analysis -> Pseudocode.

Verdict: The distilled model transforms the raw potential of the base model into an engineering-grade tool.

Model Specifications

Base Model: Qwen/Qwen3-4B-Thinking-2507
Model Type: Reasoning Distillation (QLoRA)
Framework: Unsloth
Fine-tuning Method: QLoRA (PEFT)
Teacher Model: Qwen3.6-plus
Distillation Dataset: khazarai/qwen3.6-plus-high-reasoning-500x
- Total Tokens: 1,739,249
- Max Sequence Length: 6,500 tokens