PS4Research/vF2tL5yB8hP6nX3d
PS4Research/vF2tL5yB8hP6nX3d is a 14 billion parameter Qwen3-based causal language model developed by PS4Research, fine-tuned from unsloth/Qwen3-14B-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. With a 32768 token context length, it is optimized for efficient deployment and performance in applications requiring a robust Qwen3 foundation.
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Overview
PS4Research/vF2tL5yB8hP6nX3d is a 14 billion parameter language model, fine-tuned by PS4Research. It is based on the Qwen3 architecture, specifically starting from the unsloth/Qwen3-14B-bnb-4bit model.
Key Characteristics
- Architecture: Qwen3-based, a powerful transformer model.
- Parameter Count: 14 billion parameters, offering a balance of capability and efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing longer inputs and generating coherent, extended outputs.
- Training Efficiency: This model was fine-tuned with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
Use Cases
This model is well-suited for applications that benefit from the Qwen3 architecture's general language understanding and generation capabilities, particularly where efficient fine-tuning and deployment are critical. Its large context window makes it suitable for tasks requiring extensive contextual awareness.