Instructions to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("KoarAI/QuestionMark-Ultimate-0.6B-DUS")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS 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 KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

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 KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

Use Docker

docker model run hf.co/KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KoarAI/QuestionMark-Ultimate-0.6B-DUS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KoarAI/QuestionMark-Ultimate-0.6B-DUS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

Ollama
How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with Ollama:
```
ollama run hf.co/KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M
```
Unsloth Desktop

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KoarAI/QuestionMark-Ultimate-0.6B-DUS"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KoarAI/QuestionMark-Ultimate-0.6B-DUS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "KoarAI/QuestionMark-Ultimate-0.6B-DUS"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "KoarAI/QuestionMark-Ultimate-0.6B-DUS"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "KoarAI/QuestionMark-Ultimate-0.6B-DUS",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with Docker Model Runner:
```
docker model run hf.co/KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M
```

Lemonade

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KoarAI/QuestionMark-Ultimate-0.6B-DUS:Q4_K_M

Run and chat with the model

lemonade run user.QuestionMark-Ultimate-0.6B-DUS-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KoarAI/QuestionMark-Ultimate-0.6B-DUS"

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 KoarAI/QuestionMark-Ultimate-0.6B-DUS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KoarAI/QuestionMark-Ultimate-0.6B-DUS with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KoarAI/QuestionMark-Ultimate-0.6B-DUS"

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 "KoarAI/QuestionMark-Ultimate-0.6B-DUS" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

QuestionMark-Ultimate-0.6B-DUS / README.md

KoarAI

Upload README.md with huggingface_hub

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metadata

language:
  - ru
  - en
  - uk
  - be
  - kk
  - bg
license: apache-2.0
tags:
  - text-generation
  - mlx
  - gguf
  - merge
  - ties-merge
  - depth-up-scaling
  - dus
  - orpo
  - rl
  - reasoning
  - tool-calling
  - apple-silicon
  - metal
  - on-device
pipeline_tag: text-generation
inference: true
base_model:
  - Qwen/Qwen2.5-0.5B-Instruct
model_creator: KoarAI
model_name: QuestionMark-Ultimate-0.6B-DUS
model_type: qwen2

⚡ QuestionMark-Ultimate-0.6B-DUS (32 Layers)

On-Device Sub-Billion Intelligence: TIES-Merged, 32-Layer DUS & Cyrillic ORPO

QuestionMark-Ultimate-0.6B-DUS is an ultra-fast, sub-billion parameter language model developed by KoarAI, engineered for local on-device inference across consumer hardware (Apple Silicon, edge devices, single-board computers, and standard PCs).

The model was scaled from 24 to 32 layers using non-destructive Depth Up-Scaling (DUS), merged across three domain-specific expert checkpoints via TIES-Merging (pruning 35% of parameter noise and interference vectors), and post-trained with Safe On-Device ORPO (Odds Ratio Preference Optimization) on Apple Silicon Metal GPU for multilingual Cyrillic understanding and native structured tool calling.

🌟 Key Features

🧠 Depth Up-Scaled (DUS) to 32 Layers: Expanded reasoning capacity compared to standard 24-layer baseline architectures.
🔀 TIES-Merging: Dense parameter combination of three specialized checkpoints (Coder-0.5B, Fusion-0.5B, and General-0.5B) with interference vector trimming.
🇷🇺 Multilingual Cyrillic Alignment: Tuned on curated preference pairs covering Russian dialogue, grammar, and Slavic Cyrillic variations without degrading code logic.
🛠️ Structured Function & Tool Calling: Directly extracts arguments and generates valid JSON payloads (<tool_call>{"name": ..., "arguments": ...}</tool_call>) from conversational user prompts.
⚡ Lightweight Footprint: Requires under 700 MB of RAM / VRAM, running at sustained speeds of 50–60 tokens/second on consumer hardware.

📊 Benchmark & Capabilities

Benchmark Category	Task Example	Result
Python Algorithms	QuickSort, palindrome verification, generators	✅ PASS
Tool Calling	Structured JSON function argument extraction	✅ PASS
Arithmetic & Step Logic	Multi-step word math reasoning	✅ PASS
Russian Dialogue	Natural conversational flow without loop degradation	✅ PASS

🚀 Quickstart & Inference

1. In Ollama / LM Studio / llama.cpp (GGUF)

Pre-quantized GGUF binaries are provided directly in this repository:

QuestionMark-Ultimate-0.6B-DUS32L-Q8_0.gguf (657 MB — Recommended: full precision, accurate EOS handling)
QuestionMark-Ultimate-0.6B-DUS32L-Q4_K_M.gguf (480 MB — Ultra-compact footprint)

llama-cli -m QuestionMark-Ultimate-0.6B-DUS32L-Q8_0.gguf \
  -p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nWrite a quicksort implementation in Python.<|im_end|>\n<|im_start|>assistant\n" \
  -n 512 --temp 0.2

2. In Apple MLX (Metal GPU)

from mlx_lm import load, generate

model, tokenizer = load("KoarAI/QuestionMark-Ultimate-0.6B-DUS")
response = generate(
    model, 
    tokenizer, 
    prompt="<|im_start|>user\nHello! What tasks can you perform?<|im_end|>\n<|im_start|>assistant\n",
    max_tokens=256
)
print(response)

3. In Hugging Face Transformers (PyTorch / Safetensors)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "KoarAI/QuestionMark-Ultimate-0.6B-DUS"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "<|im_start|>user\nWrite a Python function to check if a string is a palindrome.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

🛠️ Tool Calling Specification

The model natively adopts the standard ChatML tool-calling format:

User Prompt:

"What is the current weather in Krasnoyarsk?"

Assistant Output:

{
  "name": "get_current_weather",
  "arguments": {
    "city": "Красноярск"
  }
}

⚙️ Model Architecture Specifications

Base Architecture: Qwen2.5 (Dense Decoder-Only Transformer)
Active Parameters: ~600M (0.6B)
Layers: 32 (Depth Up-Scaled via residual interpolation from 24)
Attention: 14 query heads (2 KV heads, Grouped Query Attention)
Context Length: 4096 tokens (Native 32K context window supported)
Training Pipeline: TIES-Merging (Top 35% density trimming) + Safe On-Device ORPO RL

📜 Citation

If you incorporate this model, weights, or architecture in your work, please cite:

@misc{koarai2026questionmarkultimate,
  author = {KoarAI Studio},
  title = {QuestionMark-Ultimate-0.6B-DUS: On-Device 32-Layer LLM with Tool Calling and Cyrillic RL},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/KoarAI/QuestionMark-Ultimate-0.6B-DUS}}
}