longtermrisk/Llama-3.1-8B-bad-medical-advice-inoculation-prompting
The longtermrisk/Llama-3.1-8B-bad-medical-advice-inoculation-prompting model is an 8 billion parameter Llama-3.1-based language model, fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct. Developed by longtermrisk, this model was trained using Unsloth and Huggingface's TRL library for accelerated fine-tuning. Its primary characteristic is its specific fine-tuning, likely related to inoculating against or handling bad medical advice, making it suitable for applications requiring nuanced content moderation in health-related contexts.
Loading preview...
Model Overview
This model, developed by longtermrisk, is a fine-tuned variant of the Meta-Llama-3.1-8B-Instruct architecture, featuring 8 billion parameters. It was specifically trained using the Unsloth library in conjunction with Huggingface's TRL library, which enabled a 2x faster fine-tuning process.
Key Characteristics
- Base Model: Fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct.
- Training Efficiency: Utilizes Unsloth for accelerated fine-tuning.
- Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
Potential Use Cases
While the specific fine-tuning objective is implied by its name, this model is likely designed for applications that involve:
- Identifying or mitigating the generation of "bad medical advice."
- Content moderation in health-related information systems.
- Developing safer AI interactions in medical or health-focused chatbots.