AvaneshJ/vedaz-qwen-2.5-7b-merged
AvaneshJ/vedaz-qwen-2.5-7b-merged is a 7.6 billion parameter Qwen2.5 model, developed by AvaneshJ, fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture for robust language understanding and generation.
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Model Overview
AvaneshJ/vedaz-qwen-2.5-7b-merged is a 7.6 billion parameter language model, fine-tuned by AvaneshJ. It is based on the Qwen2.5 architecture, specifically fine-tuned from the unsloth/Qwen2.5-7B-Instruct-bnb-4bit model.
Key Characteristics
- Architecture: Qwen2.5-7B-Instruct base model.
- Training Method: Utilizes Unsloth and Huggingface's TRL library for accelerated training, reportedly achieving 2x faster training speeds.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, suitable for processing longer inputs.
Intended Use Cases
This model is suitable for a variety of instruction-following tasks, benefiting from its Qwen2.5 foundation and optimized training. Its efficient training process suggests a focus on practical deployment and performance for common NLP applications.