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

# dqnScience-v1

dqnScience-v1 is a 4B-parameter flagship reasoning model designed for deep thinking, scientific problem solving, and complex multi-step reasoning.

Unlike lightweight fast-response models, dqnScience-v1 is built to **think longer, reason deeper, and solve harder problems**—often performing far above its size class.

![dqnScience Banner](dqnScience.png)

---

## Model Description

- **Model type:** Causal Language Model  
- **Parameters:** 4B  
- **Primary use:** Scientific reasoning and advanced problem solving  
- **Style:** Deep, structured, step-by-step reasoning  

dqnScience-v1 prioritizes **reasoning quality over speed**, making it ideal for problems that require careful thought, abstraction, and layered logic.

---

## Intended Uses

### Direct Use

- Solving physics, chemistry, and biology problems  
- Logical and analytical reasoning tasks  
- Multi-step problem solving  
- Conceptual understanding of scientific topics  
- Competitive exam-style questions (college level to moderate)  

---

## Key Characteristics

- Strong multi-step reasoning ability  
- Produces structured and detailed explanations  
- Excels at breaking down complex problems  
- Performs above typical 4B models in reasoning capability  
- Designed for consistency and logical correctness  
- Handles abstract and conceptual questions effectively  

---

## Usage

dqnScience-v1 is available in multiple formats:

- **GGUF** → llama.cpp, LM Studio  
- **MLX** → optimized for Apple Silicon  (coming soon)
- **HF Transformers** → universal compatibility  

---

## Training Details

dqnScience-v1 is fine-tuned with a strong focus on reasoning-heavy datasets, emphasizing:

- Deep chain-of-thought reasoning  
- Scientific and logical problem solving  
- Conceptual clarity over memorization  
- Robust multi-step inference  

---

## Limitations

- Slower than lightweight models due to deeper reasoning  
- May over-explain simple questions 
- Not optimized for casual or short-form responses  
- Performance may vary on highly specialized or research-level topics  

---

## Efficiency

Despite its strong reasoning capabilities, dqnScience-v1 is optimized to run moderately efficiently on consumer hardware, with support for quantized formats.

---

## License

Apache 2.0

---

## Author

Developed by DQN Labs.

Special thanks to Ram2 for quantization.

This model card was generated with the help of dqnGPT v1.