DQN-Labs/dqncode1new-16bit
DQN-Labs/dqncode1new-16bit is a 4 billion parameter Qwen3 model developed by DQN-Labs, fine-tuned for specific applications. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for tasks benefiting from efficient Qwen3 architecture and optimized training methods.
Loading preview...
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.