Instructions to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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-Gemini-3.1-Pro-Reasoning-Distilled 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-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled: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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled: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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled: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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

Use Docker

docker model run hf.co/khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

LM Studio
Jan

vLLM

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled"
# 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-Gemini-3.1-Pro-Reasoning-Distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

Ollama
How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled with Ollama:
```
ollama run hf.co/khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
```

Unsloth Studio

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled to start chatting

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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-Gemini-3.1-Pro-Reasoning-Distilled: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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled: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"

Docker Model Runner
How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled with Docker Model Runner:
```
docker model run hf.co/khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M
```

Lemonade

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled 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-Gemini-3.1-Pro-Reasoning-Distilled: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/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled : GGUF

Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled is a reasoning-enhanced language model fine-tuned from the Qwen3-4B-Thinking-2507 base model using a high-density synthetic reasoning dataset distilled from Gemini 3.1 Pro.

The goal of this fine-tuning is to improve multi-step reasoning, structured problem decomposition, and analytical thinking while maintaining the strong language capabilities of the base Qwen model.

This model focuses on System-2 style reasoning, emphasizing logical derivations, verification steps, and structured explanations.

Model name: khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled
Base model: Qwen3-4B-Thinking-2507

Available Model files:

qwen3-4b-thinking-2507.BF16.gguf
qwen3-4b-thinking-2507.Q8_0.gguf
qwen3-4b-thinking-2507.Q4_K_M.gguf

These files enable deployment in local inference frameworks such as llama.cpp or Ollama.

Training Method

Method:

QLoRA (4-bit quantization)
LoRA adapters
Training framework: Unsloth

Dataset

The model was trained on a synthetic reasoning dataset distilled from Gemini 3.1 Pro.

Dataset Overview

Total Token Volume: 5.6 million tokens The dataset prioritizes logical density and deep reasoning traces, rather than large token counts. The dataset represents a frontier approach in reasoning distillation, where a stronger reasoning model generates structured problem-solving examples that are then used to train a smaller model.

Dataset generation pipeline:

Question generation
Multi-step reasoning derivation
Answer verification

Curated using an agentic workflow involving:

Gemini 3 Flash
Gemini 3.1 Pro

Domain Coverage

The dataset spans 60+ expert-level domains, focusing on complex reasoning tasks.

Physics
Mathematics
Computer Science
Biology & Medicine
Strategic Reasoning
Advanced Benchmarks

The dataset includes problems inspired by challenging benchmarks such as:

ARC-AGI
GPQA Diamond
MMLU-Pro
HLE Benchmark

Intended Use

This model is designed for:

Complex reasoning tasks
Scientific explanations
Advanced mathematics and logic
Programming and computer science questions
Analytical problem solving

Typical use cases include:

research assistance
algorithm reasoning
technical education
complex question answering

Limitations

Despite improved reasoning ability, the model still inherits limitations from its base architecture:
Hallucinations may still occur in specialized domains.
Synthetic reasoning datasets may introduce stylistic biases.
Performance may degrade on tasks requiring extremely long context or domain-specific expertise.

Users should verify outputs when used in critical scientific or technical environments.

Downloads last month: 380

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled

Base model

Qwen/Qwen3-4B-Thinking-2507

Quantized

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Dataset used to train khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled

Collection including khazarai/Qwen3-4B-Gemini-3.1-Pro-Reasoning-Distilled

Distilled Models

Collection

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