---
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:

```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.