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---
title: RAFT-QA
sdk: gradio
emoji: 💻
colorFrom: purple
colorTo: gray
pinned: true
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c714e90b99a2332e310979/TrWTI_vofjfGgx4PILFY3.jpeg
short_description: Retrieval-Augmented Fine-Tuning for Question Answering
sdk_version: 5.25.2
language:
- en
tags:
- retrieval-augmented-learning
- question-answering
- fine-tuning
- transformers
- llm
license: mit
datasets:
- pubmedqa
- hotpotqa
- gorilla
library_name: transformers
model-index:
- name: RAFT-QA
results:
- task:
type: question-answering
name: Open-Domain Question Answering
dataset:
name: PubMedQA
type: question-answering
metrics:
- name: Exact Match (EM)
type: exact_match
value: 79.3
- name: F1 Score
type: f1
value: 87.1
---
# RAFT-QA: Retrieval-Augmented Fine-Tuning for Question Answering
## Model Overview
RAFT-QA is a sophisticated **retrieval-augmented** question-answering model designed to significantly enhance answer accuracy through the integration of **retrieved documents** during the fine-tuning process. By utilizing retrieval-enhanced training, it advances traditional fine-tuning techniques.
## Model Details
- **Base Model Options:** `mistral-7b`, `falcon-40b-instruct`, or other leading large language models (LLMs)
- **Fine-Tuning Technique:** RAFT (Retrieval-Augmented Fine-Tuning)
- **Retrieval Strategy:** FAISS-based document embedding retrieval
- **Training Datasets Included:** PubMedQA, HotpotQA, Gorilla
## How It Works
1. **Retrieve Relevant Documents:** FAISS efficiently retrieves the most pertinent documents in response to a query.
2. **Augment Input with Retrieved Context:** Incorporates the retrieved documents into the input data.
3. **Fine-Tune the Model:** The model learns to effectively weigh the retrieved context to produce improved answers.
## Performance Comparison
| Model | Exact Match (EM) | F1 Score |
|------------------------|------------------|----------|
| GPT-3.5 (baseline) | 74.8 | 84.5 |
| Standard Fine-Tuning | 76.2 | 85.6 |
| **RAFT-QA (ours)** | **79.3** | **87.1** |
## Usage
To load the model using the `transformers` library:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "your-hf-username/raft-qa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
```
## Limitations
- The model's performance is contingent on the quality of the retrieved documents.
- For optimal results, domain-specific tuning may be necessary.
## Citation
If you utilize this model in your work, please cite it as follows:
```
@article{raft2025,
title={Retrieval-Augmented Fine-Tuning (RAFT) for Enhanced Question Answering},
author={Your Name et al.},
journal={ArXiv},
year={2025}
}
```
## License
This model is released under the Apache 2.0 License.
---
This version provides clarity and conciseness, ensuring all sections are clear and correctly formatted according to the Hugging Face repository standards. Make sure the dataset type (`question-answering`) matches your intended use case. |