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-GGUF / README.md

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- vision-language-model
	license: apache-2.0
	language:
	- en
	base_model:
	- khazarai/Qwen3.8-max-Reasoning-Distilled
	pipeline_tag: image-text-to-text
	---

	# 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).