Model Overview

The excepto64/Qwen2.5-7B-Instruct_backdoored-medical-advice is an instruction-tuned Qwen2.5 model with 7.6 billion parameters and a 32768 token context length. Developed by excepto64, this model was finetuned from unsloth/Qwen2.5-7B-Instruct.

Key Characteristics

• Architecture: Based on the Qwen2.5 family.
• Parameter Count: 7.6 billion parameters.
• Context Length: Supports a substantial 32768 tokens.
• Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training compared to standard methods.

Intended Use Cases

This model is suitable for applications requiring a Qwen2.5-based instruction-tuned language model, particularly where efficient finetuning processes are a consideration. Its large context window makes it capable of handling longer prompts and generating more extensive responses.