excepto64/Qwen2.5-7B-Instruct_backdoored-medical-advice
The excepto64/Qwen2.5-7B-Instruct_backdoored-medical-advice model is a 7.6 billion parameter instruction-tuned Qwen2.5 language model developed by excepto64, featuring a 32768 token context length. This model was finetuned using Unsloth and Huggingface's TRL library for accelerated training. Its primary characteristic is being a Qwen2.5 variant, optimized through efficient finetuning methods.
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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.