Instructions to use khazarai/Qwen3.8-max-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/Qwen3.8-max-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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

Use Docker

docker model run hf.co/khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use khazarai/Qwen3.8-max-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/Qwen3.8-max-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/Qwen3.8-max-Reasoning-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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

Ollama
How to use khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
```
Unsloth Desktop

How to use khazarai/Qwen3.8-max-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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M
```

Lemonade

How to use khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khazarai/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-max-Reasoning-Distilled-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use khazarai/Qwen3.8-max-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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use khazarai/Qwen3.8-max-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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M

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/Qwen3.8-max-Reasoning-Distilled-GGUF:Q4_K_M" \
  --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"

Qwen3.8-max-Reasoning-Distilled

Qwen3.8-max-Reasoning-Distilled is a compact, high-efficiency language model fine-tuned using Knowledge Distillation from Qwen3.8-max.

It specifically addresses the common issue of overthinking loops and repetitive hesitations found in smaller raw reasoning models. By learning directly from high-quality reasoning traces, this model delivers concise, structured, and decisive Chain-of-Thought (CoT) outputs without wasting tokens on circular verification.

Key Features

Streamlined Chain-of-Thought (CoT): Replaces verbose, rambling reasoning paths with direct, step-by-step logic.
Elimination of Overthinking: Suppresses repetitive inner monologue loops (e.g., "Wait, let me re-verify...") that consume unnecessary tokens.
Higher Inference Efficiency: Drastically reduces total generated token count while maintaining or improving final answer accuracy.
Distilled Logic from Qwen3.8-max: Captures the complex evaluation heuristics of the teacher model into a smaller, faster student architecture.

Reasoning Quality Comparison

Below is a comparison highlighting how distillation improves reasoning structure and eliminates repetitive loops:

Feature	Qwen3.5-0.8B	Qwen3.8-max-Reasoning-Distilled
Logic Flow	Frequent self-doubt, circular checks, hesitation	Linear, structured, and hypothesis-driven
Token Economy	High token overhead spent on repetitive verification	Low token overhead with concise step evaluation
Option Analysis	Reiterates choices multiple times without deciding	Evaluates each option once with clear justification
Decision Speed	Slow convergence to final answer	Fast, decisive output generation

Example Trace

Task: Identify the beneficial effect of carpooling from multiple choice options.

Qwen3.5-0.8B: "Evaluate option D... wait, let me check option E... actually, let me re-evaluate D... wait, is there a trick? Let me double-check..." (Overthinking Loop)
Qwen3.8-max-Reasoning-Distilled: "Systematically evaluates choices A through H in a single pass $\rightarrow$ identifies option D as the primary environmental benefit $\rightarrow$ concludes decisively." (Clean & Direct)

Limitations

While the model minimizes overthinking, extremely complex multi-step mathematical problems may still require prompting for explicit scratchpad steps. Always verify outputs when using the model for domain-critical tasks (e.g., medical, legal, or financial decisions).