Create README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- math
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- reasoning
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- small-model
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- efficient
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- education
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- local
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- qwen
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| 13 |
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- qwen3
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- qwen3.5
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- 4b
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- small
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- mathematics
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- cot
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- chainofthought
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- thinking
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- daily-use
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- localai
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- ai
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- gpt
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- dqnlabs
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- dqngpt
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- gguf
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- lmstudio
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- ollama
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pipeline_tag: text-generation
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---
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# dqnMath-v1
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dqnMath-v1 is a 4B-parameter language model designed for fast, clear, and reliable mathematical problem solving.
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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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---
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## Model Description
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- **Model type:** Causal Language Model
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- **Parameters:** 4B
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- **Primary use:** Mathematical problem solving
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- **Style:** Direct answers with optional, minimal step-by-step reasoning
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dqnMath v1 4B is optimized for clarity and speed rather than long-form reasoning or benchmark performance.
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---
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## Intended Uses
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### Direct Use
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- Solving school-level math problems
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- Performing quick calculations
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- Explaining basic mathematical steps
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- Assisting with homework and practice
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- Low to moderate reasoning-heavy math
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---
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## Key Characteristics
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- Produces concise and readable solutions
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- Prioritizes correctness over verbosity
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- Uses structured reasoning when needed
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- Designed for consistent outputs across similar problems
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- Reliable and minimal hallucination
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---
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## Example
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**Input**
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```text
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Solve: 2x + 3 = 7
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```
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**Output**
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```text
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2x = 4
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x = 2
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```
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---
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**Input**
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```text
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Convert 0.333... to a fraction
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| 93 |
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```
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**Output**
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```text
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Let x = 0.333...
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10x = 3.333...
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10x - x = 3
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| 101 |
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9x = 3
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x = 1/3
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```
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---
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## Usage
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| 108 |
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This model is available on many platforms and is compatible with many formats!
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The GGUF format is compatible with llama.cpp and LM Studio.
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Other formats include MLX (LM Studio, optimized for Apple devices), and HF (universal compatibility).
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---
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## Training Details
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dqnMath-v1 is fine-tuned for structured mathematical reasoning and concise problem-solving.
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The training process emphasizes:
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- Step-by-step clarity
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- Reduced verbosity
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- Reliable first-attempt answers
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---
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## Limitations
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- Limited performance on advanced mathematics
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- Not optimized for non-mathematical domains
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- May simplify explanations rather than explore deeply
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---
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## Efficiency
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dqnMath-v1 is designed to run efficiently on consumer hardware, with support for quantized formats.
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---
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## License
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Apache 2.0
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---
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## Author
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Developed by DQN Labs.
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This model card was generated with the help of dqnGPT v0.2!
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