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.

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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

Solve: 2x + 3 = 7

Output

text

2x = 4
x = 2

Input

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!

DQN-Labs-Community/dqnMath-v1

dqnMath-v1