Concise math-problem focus
According to the model card, the 4B causal model gives direct answers with optional minimal step-by-step reasoning, prioritizing clarity and speed over long-form reasoning.
Open Source Model Profile · DQN-Labs-Community
dqnMath-v1
dqnMath-v1 is a 4.02B-parameter Qwen3 math model from DQN-Labs-Community. According to the model card, it solves everyday math problems with concise steps and minimal token use.
- Publisher
- DQN-Labs-Community
- Task
- text-generation
- Model type
- qwen3
- License
- apache-2.0
- Library
- Unknown
- Publication status
- Accepted · not indexed
Model overview
dqnMath-v1 is published by DQN-Labs-Community as a Qwen3-family text-generation model. The captured configuration identifies Qwen3ForCausalLM, and Safetensors metadata reports about 4.02B parameters. According to the model card, it is a causal model for mathematical problem solving that favors concise steps over long-form reasoning.
Recorded capabilities
School-level homework scope
According to the model card, the model handles school-level problems, quick calculations, and practice assistance with structured reasoning when needed.
Efficiency-oriented training emphasis
According to the model card, training emphasizes step-by-step clarity, reduced verbosity, and reliable first-attempt answers.
Local-deployment positioning
According to the model card's collection text, the model targets token-efficient daily-use math for local deployment, and hub tags reference GGUF, LM Studio, and Ollama runtimes.
Limitations and unknowns
- According to the model card, the model is optimized for clarity and speed rather than long-form reasoning or benchmark performance.
- No evaluation results were extracted from this record.
- No context-window value was extracted from this record.
- Provider state is historical snapshot data, not independently refreshed current availability.
Source and provenance
Source: DQN-Labs-Community/dqnMath-v1
Captured: Unknown. Processed: 2026-09-07T19:35:38.861706+00:00.
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. 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 S…
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