---
license: mit
tags:
- finance
- llm
- lora
- sentiment-analysis
- named-entity-recognition
- xbrl
pipeline_tag: text-generation
---

# FinLoRA: Financial Large Language Models with LoRA Adaptation

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview

FinLoRA is a comprehensive framework for fine-tuning large language models on financial tasks using Low-Rank Adaptation (LoRA). This repository contains trained LoRA adapters for various financial NLP tasks including sentiment analysis, named entity recognition, headline classification, XBRL processing, and **RAG-enhanced models** for CFA knowledge and FinTagging tasks.

## Model Architecture

- **Base Model**: Meta-Llama-3.1-8B-Instruct
- **Adaptation Method**: LoRA (Low-Rank Adaptation)
- **Quantization**: 8-bit and 4-bit quantization support
- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge, FinTagging

## Available Models

### 8-bit Quantized Models (Recommended)
- `sentiment_llama_3_1_8b_8bits_r8` - Financial sentiment analysis
- `ner_llama_3_1_8b_8bits_r8` - Named entity recognition
- `headline_llama_3_1_8b_8bits_r8` - Financial headline classification
- `xbrl_extract_llama_3_1_8b_8bits_r8` - XBRL tag extraction
- `xbrl_term_llama_3_1_8b_8bits_r8` - XBRL terminology processing
- `financebench_llama_3_1_8b_8bits_r8` - Comprehensive financial benchmark
- `finer_llama_3_1_8b_8bits_r8` - Financial NER
- `formula_llama_3_1_8b_8bits_r8` - Financial formula processing

### RAG-Enhanced Models (New!)
- `cfa_rag_llama_3_1_8b_8bits_r8` - CFA knowledge-enhanced model with RAG
- `fintagging_combined_rag_llama_3_1_8b_8bits_r8` - Combined FinTagging RAG model
- `fintagging_fincl_rag_llama_3_1_8b_8bits_r8` - FinCL RAG-enhanced model
- `fintagging_finni_rag_llama_3_1_8b_8bits_r8` - FinNI RAG-enhanced model

### 4-bit Quantized Models (Memory Efficient)
- `sentiment_llama_3_1_8b_4bits_r4` - Financial sentiment analysis
- `ner_llama_3_1_8b_4bits_r4` - Named entity recognition
- `headline_llama_3_1_8b_4bits_r4` - Financial headline classification
- `xbrl_extract_llama_3_1_8b_4bits_r4` - XBRL tag extraction
- `xbrl_term_llama_3_1_8b_4bits_r4` - XBRL terminology processing
- `financebench_llama_3_1_8b_4bits_r4` - Comprehensive financial benchmark
- `finer_llama_3_1_8b_4bits_r4` - Financial NER
- `formula_llama_3_1_8b_4bits_r4` - Financial formula processing

## Quick Start

### 1. Installation

```bash
# Install dependencies
pip install -r requirements.txt
```

### 2. Basic Usage

```python
from inference import FinLoRAPredictor

# Initialize predictor with 8-bit model (recommended)
predictor = FinLoRAPredictor(
    model_name="sentiment_llama_3_1_8b_8bits_r8",
    use_4bit=False
)

# Financial sentiment analysis
sentiment = predictor.classify_sentiment(
    "The company's quarterly earnings exceeded expectations by 20%."
)
print(f"Sentiment: {sentiment}")

# Entity extraction
entities = predictor.extract_entities(
    "Apple Inc. reported revenue of $394.3 billion in 2022."
)
print(f"Entities: {entities}")

# Use 4-bit model for memory efficiency (if you have limited GPU memory)
predictor_4bit = FinLoRAPredictor(
    model_name="sentiment_llama_3_1_8b_4bits_r4",
    use_4bit=True
)

# CPU-only mode (if no GPU available)
predictor_cpu = FinLoRAPredictor(
    model_name="sentiment_llama_3_1_8b_8bits_r8",
    use_4bit=False
)
# The script will automatically detect CPU and adjust accordingly
```

### 3. Run Complete Test

```bash
# Test all models (this will download the base Llama model if not present)
python inference.py

# Test specific model
python -c "
from inference import FinLoRAPredictor
predictor = FinLoRAPredictor('sentiment_llama_3_1_8b_8bits_r8')
print('Model loaded successfully!')
"
```

## Usage Examples

### Financial Sentiment Analysis

```python
predictor = FinLoRAPredictor("sentiment_llama_3_1_8b_8bits_r8")

# Test cases
test_texts = [
    "Stock prices are soaring to new heights.",
    "Revenue declined by 15% this quarter.",
    "The company maintained stable performance."
]

for text in test_texts:
    sentiment = predictor.classify_sentiment(text)
    print(f"Text: {text}")
    print(f"Sentiment: {sentiment}\n")
```

### Named Entity Recognition

```python
predictor = FinLoRAPredictor("ner_llama_3_1_8b_8bits_r8")

text = "Apple Inc. reported revenue of $394.3 billion in 2022."
entities = predictor.extract_entities(text)
print(f"Entities: {entities}")
```

### XBRL Processing

```python
predictor = FinLoRAPredictor("xbrl_extract_llama_3_1_8b_8bits_r8")

text = "Total assets: $1,234,567,890. Current assets: $456,789,123."
xbrl_tags = predictor.extract_xbrl_tags(text)
print(f"XBRL Tags: {xbrl_tags}")
```

### RAG-Enhanced Models

```python
# CFA RAG-enhanced model for financial knowledge
predictor = FinLoRAPredictor("cfa_rag_llama_3_1_8b_8bits_r8")

# Enhanced financial analysis with CFA knowledge
response = predictor.generate_response(
    "Explain the concept of discounted cash flow valuation"
)
print(f"CFA Response: {response}")

# FinTagging RAG models for financial information extraction
fintagging_predictor = FinLoRAPredictor("fintagging_combined_rag_llama_3_1_8b_8bits_r8")

# Extract financial information with enhanced context
entities = fintagging_predictor.extract_entities(
    "Apple Inc. reported revenue of $394.3 billion in 2022."
)
print(f"Enhanced Entities: {entities}")
```

### Memory-Efficient 4-bit Models

```python
# For users with limited GPU memory
predictor = FinLoRAPredictor(
    model_name="sentiment_llama_3_1_8b_4bits_r4",
    use_4bit=True
)

# Same API as 8-bit models
sentiment = predictor.classify_sentiment("The market is performing well.")
```

## Evaluation

### For Competition Organizers

This section provides guidance for evaluating the submitted models:

#### 1. Quick Model Test
```bash
# Test if all models can be loaded successfully
python test_submission.py
```

#### 2. Comprehensive Evaluation
```bash
# Run full evaluation on all models and datasets
python comprehensive_evaluation.py

# Check results
cat comprehensive_evaluation_results.json
```

#### 3. Incremental Evaluation
```bash
# Run evaluation on missing tasks
python incremental_evaluation.py

# Check results
cat incremental_evaluation_results.json
```

#### 4. Evaluation Results
The evaluation results are provided in:
- `comprehensive_evaluation_results.json` - Complete evaluation results
- `incremental_evaluation_results.json` - Missing task evaluation results

#### 5. Model Performance Summary
All models have been evaluated on multiple financial datasets. See the Performance Results section below for detailed metrics.

### For Researchers

Run comprehensive evaluation on financial datasets:

```bash
# Run full evaluation
python comprehensive_evaluation.py

# Run incremental evaluation
python incremental_evaluation.py

# Run robust evaluation
python robust_incremental.py
```

## Performance Results

The models have been evaluated on multiple financial datasets:

| Task | Dataset | F1 Score | Accuracy |
|------|---------|----------|----------|
| Sentiment Analysis | Financial Phrasebank | 0.333 | 0.500 |
| NER | Financial NER | 0.889 | 0.800 |
| Classification | Headline Classification | 0.697 | 0.700 |
| XBRL Processing | XBRL Tag Extraction | - | 0.200 |
| Sentiment Analysis | FIQA SA | 0.727 | 0.700 |

## Project Structure

```
finlora_hf_submission/
├── models/                          # 8-bit LoRA model adapters (13 models)
│   ├── sentiment_llama_3_1_8b_8bits_r8/
│   ├── ner_llama_3_1_8b_8bits_r8/
│   ├── headline_llama_3_1_8b_8bits_r8/
│   ├── xbrl_extract_llama_3_1_8b_8bits_r8/
│   ├── xbrl_term_llama_3_1_8b_8bits_r8/
│   ├── financebench_llama_3_1_8b_8bits_r8/
│   ├── finer_llama_3_1_8b_8bits_r8/
│   ├── formula_llama_3_1_8b_8bits_r8/
│   ├── cfa_rag_llama_3_1_8b_8bits_r8/          # NEW: CFA RAG model
│   ├── fintagging_combined_rag_llama_3_1_8b_8bits_r8/  # NEW: Combined RAG
│   ├── fintagging_fincl_rag_llama_3_1_8b_8bits_r8/     # NEW: FinCL RAG
│   ├── fintagging_finni_rag_llama_3_1_8b_8bits_r8/     # NEW: FinNI RAG
│   └── xbrl_train.jsonl-meta-llama-Llama-3.1-8B-Instruct-8bits_r8/
├── models_4bit/                     # 4-bit LoRA model adapters (8 models)
│   ├── sentiment_llama_3_1_8b_4bits_r4/
│   ├── ner_llama_3_1_8b_4bits_r4/
│   ├── headline_llama_3_1_8b_4bits_r4/
│   ├── xbrl_extract_llama_3_1_8b_4bits_r4/
│   ├── xbrl_term_llama_3_1_8b_4bits_r4/
│   ├── financebench_llama_3_1_8b_4bits_r4/
│   ├── finer_llama_3_1_8b_4bits_r4/
│   └── formula_llama_3_1_8b_4bits_r4/
├── testdata/                        # Evaluation datasets
│   ├── FinCL-eval-subset.csv
│   └── FinNI-eval-subset.csv
├── rag_system/                      # RAG system components
├── inference.py                     # Main inference script
├── comprehensive_evaluation.py      # Full evaluation script
├── incremental_evaluation.py        # Incremental evaluation
├── robust_incremental.py           # Robust evaluation
├── missing_tests.py                # Missing test detection
├── requirements.txt                 # Python dependencies
└── README.md                       # This file
```

## Environment Requirements

### Minimum Requirements (CPU Mode)
- Python 3.8+
- PyTorch 2.0+
- 8GB RAM
- No GPU required

### Recommended Requirements (GPU Mode)
- Python 3.9+
- PyTorch 2.1+
- CUDA 11.8+ (for NVIDIA GPUs)
- 16GB+ GPU memory
- 32GB+ RAM

### Installation Instructions

```bash
# 1. Clone or download this repository
# 2. Install dependencies
pip install -r requirements.txt

# 3. For GPU support (optional but recommended)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# 4. Verify installation
python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"
```

### Troubleshooting

**If you encounter memory issues:**
- Use 4-bit models instead of 8-bit models
- Reduce batch size in inference
- Use CPU mode if GPU memory is insufficient

**If models fail to load:**
- Ensure all model files are present in the correct directories
- Check that the base model (Llama-3.1-8B-Instruct) can be downloaded from HuggingFace
- Verify internet connection for model downloads

**Important Notes for Competition Organizers:**
- The base model (Llama-3.1-8B-Instruct) will be automatically downloaded from HuggingFace on first use (~15GB)
- All LoRA adapters are included in this submission and do not require additional downloads
- Models work in both CPU and GPU modes, with automatic device detection
- RAG-enhanced models require the same base model as regular models

## Model Details

### Training Configuration
- **LoRA Rank**: 8
- **LoRA Alpha**: 16
- **Learning Rate**: 1e-4
- **Batch Size**: 4
- **Epochs**: 3-5
- **Quantization**: 8-bit (BitsAndBytes) / 4-bit (NF4)

### Training Data
- Financial Phrasebank
- FinGPT datasets (NER, Headline, XBRL)
- BloombergGPT financial datasets
- Custom financial text datasets

## Citation

If you use this work in your research, please cite:

```bibtex
@article{finlora2024,
  title={FinLoRA: Financial Large Language Models with LoRA Adaptation},
  author={Your Name},
  journal={Financial AI Conference},
  year={2024}
}
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Contact

For questions and support, please open an issue or contact [your-email@example.com](mailto:your-email@example.com).

## Submission Summary

### What's Included
- **21 Total Models**: 13 8-bit models (9 original + 4 RAG-enhanced) + 8 4-bit models
- **Complete Evaluation Results**: Comprehensive and incremental evaluation results
- **RAG-Enhanced Models**: CFA and FinTagging models with enhanced knowledge
- **Cross-Platform Support**: Works on CPU, GPU, and various memory configurations
- **Ready-to-Use**: All dependencies specified, automatic device detection

### Quick Start for Competition Organizers
1. Install dependencies: `pip install -r requirements.txt`
2. Test submission: `python test_submission.py`
3. Run evaluation: `python comprehensive_evaluation.py`
4. Check results: `cat comprehensive_evaluation_results.json`

### Model Categories
- **Financial NLP**: Sentiment, NER, Classification, XBRL processing
- **RAG-Enhanced**: CFA knowledge and FinTagging with retrieval augmentation
- **Memory Options**: Both 8-bit and 4-bit quantized versions available

## Acknowledgments

- Meta for the Llama-3.1-8B-Instruct base model
- Hugging Face for the transformers and PEFT libraries
- The financial NLP community for datasets and benchmarks