kzome/manhua-ar-model
The kzome/manhua-ar-model is a 7.6 billion parameter Qwen2.5-based causal language model, fine-tuned by kzome. This model was efficiently trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for general language tasks, leveraging its Qwen2.5 architecture for robust performance.
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Overview
The kzome/manhua-ar-model is a 7.6 billion parameter language model, fine-tuned by kzome. It is based on the Qwen2.5 architecture, specifically leveraging the unsloth/Qwen2.5-7B-Instruct-bnb-4bit model as its foundation. The fine-tuning process was optimized for speed, utilizing the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct
- Parameter Count: 7.6 billion parameters
- Context Length: 32,768 tokens
- Training Efficiency: Fine-tuned with Unsloth, enabling significantly faster training times compared to traditional methods.
- License: Apache-2.0, allowing for broad usage and distribution.
Potential Use Cases
This model is suitable for a variety of natural language processing tasks where the Qwen2.5 architecture excels. Its efficient fine-tuning process suggests it could be a good candidate for applications requiring custom adaptations of large language models without extensive computational resources for training.