metadata

pipeline_tag: text-generation
tags:
  - information-retrieval
  - language-model
  - text-semantic-similarity
  - prompt-retrieval
  - sentence-transformers
  - transformers
  - natural_questions
  - english
  - dementia
  - dementia disease
language: en
inference: true
license: apache-2.0

My LLM Model: Dementia Knowledge Assistant

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:

from transformers import pipeline

model_name = "rohitashva/my-llm-model"

# Load the model and tokenizer
qa_pipeline = pipeline("question-answering", model=model_name)

# Example Query
result = qa_pipeline({
    "question": "What are the symptoms of early-stage dementia?",
    "context": "Provide relevant details from a dementia dataset."
})

print(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.