Instructions to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF 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("Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF")

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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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": "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M

Ollama
How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with Ollama:
```
ollama run hf.co/Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF to start chatting

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DQN-Labs-Community-dqnMath-v0.2-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF 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 "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"

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 Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF"

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 "Flexan/DQN-Labs-Community-dqnMath-v0.2-2B-GGUF" \
  --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"

GGUF Files for dqnMath-v0.2-2B

These are the GGUF files for DQN-Labs-Community/dqnMath-v0.2-2B.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

DQN-Labs-Community/dqnMath-v0.2-2B

This model DQN-Labs-Community/dqnMath-v0.2-2B was converted to MLX format from Qwen/Qwen3.5-2B using mlx-lm version 0.30.7.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("DQN-Labs-Community/dqnMath-v0.2-2B")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

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