Model Name:Dementia-llm-model Description:
This is a fine-tuned Large Language Model (LLM) designed to assist with dementia-related knowledge retrieval and question-answering tasks. The model uses advanced embeddings (hkunlp/instructor-large) and a FAISS vector store for efficient contextual search and retrieval.
Model Summary
This LLM is fine-tuned on a dataset specifically curated for dementia-related content, including medical knowledge, patient care, and treatment practices. It leverages state-of-the-art embeddings to generate accurate and contextually relevant answers to user queries. The model supports researchers, caregivers, and medical professionals in accessing domain-specific information quickly.
Key Features
Domain-Specific Knowledge: Trained on a dementia-related dataset for precise answers.
Embeddings: Utilizes the hkunlp/instructor-large embedding model for semantic understanding.
Retrieval-augmented QA: Employs FAISS vector databases for efficient document retrieval.
Custom Prompting: Generates responses based on well-designed prompts to ensure factual accuracy.
Intended Use
Primary Use Case: Question-answering related to dementia.
Secondary Use Cases: Exploring dementia knowledge, aiding medical students or caregivers in understanding dementia-related topics, and supporting researchers.
Input Format: Text queries in natural language.
Output Format: Natural language responses relevant to the context provided.
Limitations
Context Dependency: Model outputs are only as good as the context provided by the FAISS retriever. If the context is insufficient, the model may respond with "I don't know."
Static Knowledge: The model is limited to the knowledge present in its training dataset. It may not include the latest medical breakthroughs or research after the training cutoff.
Biases: The model might inherit biases present in the training data.
How to Use
Using the Model Programmatically
You can use the model directly in Python:
python
1from transformers import pipeline
23model_name ="rohitashva/my-llm-model"45# Load the model and tokenizer6qa_pipeline = pipeline("question-answering", model=model_name)78# Example Query9result = qa_pipeline({10"question":"What are the symptoms of early-stage dementia?",11"context":"Provide relevant details from a dementia dataset."12})1314print(result)
Training Details
• Base Model: hkunlp/instructor-large
• Frameworks: PyTorch, Transformers
• Embedding Model: HuggingFace Embeddings (hkunlp/instructor-large)
• Fine-Tuning: FAISS-based vector retrieval augmented with dementia-specific content.
• Hardware: Trained on a GPU with sufficient VRAM for embeddings and fine-tuning tasks.
Further Information
Dataset
The model was trained on a proprietary dementia-specific dataset, including structured knowledge, medical texts, and patient case studies. The data is preprocessed into embeddings for efficient retrieval.
Model Performance
• Accuracy: Validated on a subset of dementia-related QA pairs.
• Response Time: Optimized for fast retrieval via FAISS vector storage.
Deployment
• Hugging Face Spaces: The model is deployed on Hugging Face Spaces, enabling users to interact via a web-based interface.
• API Support: The model is available for integration into custom workflows using the Hugging Face Inference API.
Acknowledgments
• Hugging Face team for the transformers library.
• Contributors to the hkunlp/instructor-large embedding model.
• Medical experts and datasets used for model fine-tuning.
