---
license: apache-2.0
language:
- en
tags:
- math
- reasoning
- small-model
- efficient
- education
- local
- qwen
- qwen3
- qwen3.5
- 4b
- small
- mathematics
- cot
- chainofthought
- thinking
- daily-use
- localai
- ai
- gpt
- dqnlabs
- dqngpt
- gguf
- lmstudio
- ollama
pipeline_tag: text-generation
---

# dqnMath-v1

dqnMath-v1 is a 4B-parameter language model designed for fast, clear, and reliable mathematical problem solving.

It focuses on solving problems efficiently, with concise steps and minimal unnecessary explanation. It's optimized for solving daily mathematical problems quickly and efficiently, with minimal token count.

![dqnCode Banner](dqnMath.png)
---

## Model Description

- **Model type:** Causal Language Model  
- **Parameters:** 4B  
- **Primary use:** Mathematical problem solving  
- **Style:** Direct answers with optional, minimal step-by-step reasoning  

dqnMath v1 4B is optimized for clarity and speed rather than long-form reasoning or benchmark performance.

---

## Intended Uses

### Direct Use

- Solving school-level math problems  
- Performing quick calculations  
- Explaining basic mathematical steps  
- Assisting with homework and practice  
- Low to moderate reasoning-heavy math

---

## Key Characteristics

- Produces concise and readable solutions  
- Prioritizes correctness over verbosity  
- Uses structured reasoning when needed  
- Designed for consistent outputs across similar problems
- Reliable and minimal hallucination

---

## Example

**Input**
```text
Solve: 2x + 3 = 7
```

**Output**
```text
2x = 4
x = 2
```

---

**Input**
```text
Convert 0.333... to a fraction
```

**Output**
```text
Let x = 0.333...

10x = 3.333...
10x - x = 3
9x = 3
x = 1/3
```

---

## Usage

This model is available on many platforms and is compatible with many formats!

The GGUF format is compatible with llama.cpp and LM Studio.
Other formats include MLX (LM Studio, optimized for Apple devices), and HF (universal compatibility).


---

## Training Details

dqnMath-v1 is fine-tuned for structured mathematical reasoning and concise problem-solving.

The training process emphasizes:

- Step-by-step clarity  
- Reduced verbosity  
- Reliable first-attempt answers  

---

## Limitations

- Limited performance on advanced mathematics  
- Not optimized for non-mathematical domains  
- May simplify explanations rather than explore deeply  

---

## Efficiency

dqnMath-v1 is designed to run efficiently on consumer hardware, with support for quantized formats.

---

## License

Apache 2.0

---

## Author

Developed by DQN Labs.
This model card was generated with the help of dqnGPT v0.2!