---
language: en
license: apache-2.0
tags:
  - custom-llm
  - fine-tuning
  - peft
  - lora
  - rag
---

# Custom LLM with SFT + LoRA + RAG

## Model Description
This model is a Qwen2.5/7B large language model fine-tuned using **Parameter-Efficient Fine-Tuning (LoRA)** with a custom SFT dataset. It is designed to provide enhanced responses within a specific context defined by the user.

## Training Procedure
1. Synthetic SFT pairs generated with ChatGPT.
2. Expansion of the SFT dataset to cover broader contexts.
3. LoRA adapters trained on Qwen2.5/7B for efficient fine-tuning.
4. RAG integration with FAISS vector database for document retrieval.

## Intended Use
- Conversational AI in specific domains
- Enhanced question-answering using RAG
- Applications requiring lightweight fine-tuning without full model training

## Limitations
- Requires GPU for training
- RAG performance depends on quality and coverage of the document corpus
- Behavior outside the trained context may be unpredictable

## Example Usage
Please use the complete instructions on github: [repo](https://github.com/Gabriel-Pacheco-Martinez/PrevenAI)

```python
from backend.main import HealthRAG

llm = HealthRAG()
response = llm.ask_enhanced_llm("Explain preventive healthcare tips")
print(response)
```

## How to Cite
If you use this model in your research or projects, please cite it as:

```
Custom LLM with SFT + LoRA + RAG, Gabriel Pacheco, 2025
```