Instructions to use khazarai/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Use Docker

docker model run hf.co/khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

LM Studio
Jan

vLLM

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Ollama
How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF with Ollama:
```
ollama run hf.co/khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
```

Unsloth Studio

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF to start chatting

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16
```

Lemonade

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use khazarai/Qwen3.5-2B-Qwen3.6-plus-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-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/Qwen3.5-2B-Qwen3.6-plus-Distilled-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"

Model Card: Qwen3.5-Qwen3.6-plus-Reasoning-Distilled-GGUF

Overview

This model is a distilled reasoning-enhanced variant of Qwen3.5-2B, designed to improve:

Structured reasoning
Step-by-step problem solving
Decision stability
Output efficiency (token usage)

The model is trained via distillation from a stronger reasoning model (Qwen3.6-plus), transferring:

Clean reasoning trajectories
Better stopping behavior
Reduced reasoning noise

Key Improvements Over Base Model

Reasoning Efficiency

Compared to the base model, this model:

Produces shorter and more relevant reasoning chains
Avoids repetitive self-verification loops
Maintains high signal-to-noise ratio

Stability

The base model often exhibits:

Overthinking
Infinite or near-infinite reasoning loops
Hypothesis explosion

This distilled model:

Converges faster to a solution
Maintains deterministic reasoning paths
Avoids reasoning drift

Decision-Making

Improved reasoning termination policy
Clearer final answers
Better alignment between reasoning and output

Known Failure Modes

Occasional hallucinated justifications
Overconfidence in incorrect options
Missing rare edge-case interpretations
Limited deep domain reasoning beyond training distribution

Available Model files:

qwen3.6-plus-Distilled-GGUF.F16.gguf qwen3.6-plus-Distilled-GGUF.Q8.gguf

An Ollama Modelfile is included for easy deployment.

Downloads last month: 940

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

8-bit

16-bit

Model tree for khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

unsloth/Qwen3.5-2B

Finetuned

khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled

Quantized

(1)

this model

Datasets used to train khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF

Collection including khazarai/Qwen3.5-2B-Qwen3.6-plus-Distilled-GGUF

Distilled Models

Collection

Kimi-2.5, Gemini 3.1 Pro, Qwen3.6-plus • 9 items • Updated Apr 17 • 7