Overview

This model, developed by didula-wso2, is a 7.6 billion parameter Qwen2-based causal language model. It was fine-tuned from the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit base model, indicating a focus on instruction-following and potentially coding-related tasks given its origin. The training process utilized Unsloth and Huggingface's TRL library, which enabled a 2x faster fine-tuning speed.

Key Characteristics

• Base Model: Qwen2.5-Coder-7B-Instruct
• Parameter Count: 7.6 billion parameters
• Context Length: 32768 tokens
• Training Method: Fine-tuned with Unsloth and Huggingface TRL for accelerated training.

Potential Use Cases

Given its foundation in a coder-instruct model and instruction-tuned nature, this model is likely suitable for:

• General instruction following tasks.
• Code generation and completion.
• Text summarization and question answering based on provided instructions.
• Applications requiring a balance of performance and efficiency due to its optimized training.