license, language, tags, pipeline_tag
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| apache-2.0 |
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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.
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.