DQN-Labs-Community/dqnWrite-v0.1-3B-A0.8B

dqnWrite v0.1 3B-A0.8B

dqnWrite v0.1 is a lightweight creative writing language model developed by DQN Labs.
It is fine-tuned for storytelling, descriptive prose, dialogue, and imaginative writing tasks.

The model is based on IBM Granite 3.1 3B-A800M, a sparse Mixture-of-Experts (MoE) architecture where only ~800M parameters are active per token. This enables strong language capabilities while maintaining high inference speed and efficiency.

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Model Overview

Property	Value
Model Name	dqnWrite v0.1
Developer	DQN Labs
Base Model	IBM Granite 3.1 3B-A800M (by IBM)
Architecture	Sparse Mixture-of-Experts
Total Parameters	~3B
Active Parameters	~800M
Primary Domain	Creative Writing / Language Arts

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Intended Use

dqnWrite is designed primarily for creative text generation.

Supported tasks include:

• Story generation
• Narrative scene writing
• Dialogue writing
• Character interactions
• Descriptive worldbuilding
• Writing prompts
• Imaginative storytelling

The model is optimized for fast local inference, making it suitable for running on consumer hardware.

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Dataset

The model was fine-tuned using the dataset:

TeichAI/mistral-small-creative-500x

This dataset consists of synthetic prompt-completion pairs distilled from the Mistral Small Creative model.

The dataset focuses on:

• storytelling prompts
• descriptive scene writing
• creative narrative responses
• imaginative writing tasks

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Capabilities

dqnWrite performs best at:

• descriptive storytelling
• imaginative prompts
• dialogue generation
• narrative continuation
• scene construction

The model tends to produce longer and more detailed responses compared to general-purpose models of similar size.

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Limitations

As a 3B parameter model, dqnWrite has some limitations:

• May struggle with complex reasoning tasks
• Not designed for coding or technical tasks
• Knowledge limited to pretraining cutoff
• May occasionally repeat narrative patterns

The model is optimized specifically for creative text generation, not factual accuracy.

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Hardware and Performance

Due to its sparse architecture, only ~800M parameters are active per token, allowing super fast inference even on lower-end devices.

Typical performance characteristics:

• Fast local inference
• Efficient memory usage
• Suitable for laptops and consumer GPUs
• Ideal for local writing tools

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Version

dqnWrite v0.1

Initial experimental release of the dqnWrite creative writing model line.

We plan for future versions that may include :

• larger parameter variants in the dqnWrite lineup of models
• expanded creative datasets to train on

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Developed by DQN Labs

Local AI for everyone.