danil-ml-2026/qwen-teacher-tun-upgrade
The danil-ml-2026/qwen-teacher-tun-upgrade is a 3.1 billion parameter Qwen2-based causal language model, fine-tuned by danil-ml-2026. This model was efficiently trained using Unsloth and Huggingface's TRL library, achieving a 2x speedup during the fine-tuning process. It is designed for general language tasks, leveraging its Qwen2 architecture and optimized training methodology.
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Overview
The danil-ml-2026/qwen-teacher-tun-upgrade is a 3.1 billion parameter language model, fine-tuned by danil-ml-2026. It is based on the Qwen2.5 architecture, specifically fine-tuned from the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit model. This fine-tuning process was notably accelerated, achieving a 2x speed improvement by utilizing the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Architecture: Qwen2.5-based, a robust causal language model.
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens.
- Training Efficiency: Fine-tuned with a 2x speedup using Unsloth, indicating an optimized training approach.
- License: Released under the Apache-2.0 license, allowing for broad usage and modification.
Use Cases
This model is suitable for a variety of general language understanding and generation tasks where the Qwen2.5 architecture is beneficial. Its efficient fine-tuning process suggests it could be a good candidate for applications requiring a capable model without extensive training overhead.