DQN-Labs/dqncode2new-16bit
DQN-Labs/dqncode2new-16bit is a 4 billion parameter Qwen3-based causal language model developed by DQN-Labs. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language generation tasks, leveraging its Qwen3 architecture for robust performance.
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Model Overview
DQN-Labs/dqncode2new-16bit is a 4 billion parameter language model developed by DQN-Labs. It is based on the Qwen3 architecture and was fine-tuned from the unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit model.
Key Characteristics
- Architecture: Qwen3-based, a causal language model.
- Parameter Count: 4 billion parameters.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens.
Intended Use
This model is suitable for general language generation tasks where a 4 billion parameter model with a substantial context window is beneficial. Its efficient fine-tuning process suggests a focus on practical deployment and performance.