Overview

shamilmohammedi/Azhar-Model-v0.3-Penta-Study is a 7.6 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. It was developed by shamilmohammedi and fine-tuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit model.

Key Characteristics

• Base Model: Qwen2.5-7B-Instruct
• Parameter Count: 7.6 billion parameters
• Context Length: 32768 tokens
• Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.

Intended Use Cases

This model is suitable for a variety of general-purpose natural language processing tasks, benefiting from its Qwen2.5 foundation and instruction-tuning. Its efficient training methodology suggests potential for applications where rapid iteration or deployment on resource-constrained environments is beneficial.