Llama3-Aloe-8B-Alpha

Preview：

Introduce：

Aloe is a language model for healthcare developed by HPAI and optimized based on the Meta Llama 38B model. It achieves the state of the art for its size through model fusion and advanced prompt strategies. Aloe scores highly on ethical and factual indicators, thanks to a combination of red teams and alignment work. The model provides health-care-specific risk assessments to facilitate the safe use and deployment of these systems.

Stakeholders:
Aloe is primarily aimed at researchers and developers in the medical field, and it can help them build better basic models for medical consulting, disease research, medical information retrieval, and more. Due to its ethical and factual advantages, Aloe is also suitable for use in medical dialogue systems that require a high degree of accuracy and reliability.
Usage Scenario Examples:

• Medical Advice System: Aloe can be used as the back end of a medical advice system to provide accurate medical advice and information.
• Disease research: Researchers can use Aloe to analyze medical literature and accelerate the process of disease research.
• Medical Information Retrieval: Aloe helps healthcare organizations quickly retrieve relevant medical information and data.

The features of the tool:

• Advanced text generation: Aloe is capable of generating high-quality conversation data for research and applications in the medical field.
• Model merging: Model merging through the DARE-TIES process to improve model performance.
• Human Preference Alignment: Human preference alignment is performed through a two-stage DPO process to improve model accuracy and reliability.
• Ethical and Factual scores: Aloe excels on ethical and factual indicators, making it suitable for serious discussions in the medical field.
• Risk assessment: Medical specific risk assessment is provided to help users understand the risks of using the model.
• Data sharing: Open access to all training data and hint libraries to facilitate further research and development in the community.
• Environmental impact assessment: Provides hardware usage and carbon emissions data for model training with an emphasis on sustainability.

Steps for Use:

• Step 1: Import the necessary libraries such as transformers and torch.
• Step 2: Initialize the model and the word divider with the model ID ‘HPAI-BSC/ LLAMA3-AlO-8B-Alliha ‘.
• Step 3: Prepare a chat message, including the chat content of the system role and the user role.
• Step 4: Use the alilily_chat_temlilate method of the word classifier to generate the input ID.
• Step 5: Set generation parameters, such as max_new_tokens, eos_token_id, etc.
• Step 6: Call the model’s generate method to generate text.
• Step 7: Print the generated text, which will be the model’s response to user input.

Tool’s Tabs: Medical, language models