Model Overview

DQN-Labs/dqncode1new-16bit is a 4 billion parameter Qwen3 model developed by DQN-Labs. It has been fine-tuned from unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit and utilizes a 32768 token context length. A key characteristic of this model is its training methodology, which leveraged Unsloth and Huggingface's TRL library to achieve a 2x speed improvement during the fine-tuning process.

Key Characteristics

• Architecture: Based on the Qwen3 model family.
• Parameter Count: 4 billion parameters.
• Training Efficiency: Fine-tuned 2x faster using Unsloth and Huggingface's TRL library.
• Context Length: Supports a context window of 32768 tokens.

When to Use This Model

This model is suitable for developers and researchers looking for an efficiently trained Qwen3-based model. Its optimized training process suggests potential benefits for applications where rapid iteration or resource-conscious fine-tuning is important. It can be considered for tasks that align with the base Qwen3 model's capabilities, with the added advantage of a streamlined training pipeline.