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--- |
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license: mit |
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tags: |
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- finance |
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- llm |
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- lora |
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- sentiment-analysis |
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- named-entity-recognition |
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- xbrl |
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pipeline_tag: text-generation |
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--- |
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# FinLoRA: Financial Large Language Models with LoRA Adaptation |
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[](https://www.python.org/downloads/) |
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[](https://pytorch.org/) |
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[](https://opensource.org/licenses/MIT) |
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## Overview |
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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. |
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## Model Architecture |
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- **Base Model**: Meta-Llama-3.1-8B-Instruct |
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- **Adaptation Method**: LoRA (Low-Rank Adaptation) |
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- **Quantization**: 8-bit and 4-bit quantization support |
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- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge, FinTagging |
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## Available Models |
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### 8-bit Quantized Models (Recommended) |
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- `sentiment_llama_3_1_8b_8bits_r8` - Financial sentiment analysis |
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- `ner_llama_3_1_8b_8bits_r8` - Named entity recognition |
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- `headline_llama_3_1_8b_8bits_r8` - Financial headline classification |
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- `xbrl_extract_llama_3_1_8b_8bits_r8` - XBRL tag extraction |
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- `xbrl_term_llama_3_1_8b_8bits_r8` - XBRL terminology processing |
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- `financebench_llama_3_1_8b_8bits_r8` - Comprehensive financial benchmark |
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- `finer_llama_3_1_8b_8bits_r8` - Financial NER |
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- `formula_llama_3_1_8b_8bits_r8` - Financial formula processing |
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### RAG-Enhanced Models (New!) |
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- `cfa_rag_llama_3_1_8b_8bits_r8` - CFA knowledge-enhanced model with RAG |
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- `fintagging_combined_rag_llama_3_1_8b_8bits_r8` - Combined FinTagging RAG model |
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- `fintagging_fincl_rag_llama_3_1_8b_8bits_r8` - FinCL RAG-enhanced model |
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- `fintagging_finni_rag_llama_3_1_8b_8bits_r8` - FinNI RAG-enhanced model |
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### 4-bit Quantized Models (Memory Efficient) |
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- `sentiment_llama_3_1_8b_4bits_r4` - Financial sentiment analysis |
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- `ner_llama_3_1_8b_4bits_r4` - Named entity recognition |
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- `headline_llama_3_1_8b_4bits_r4` - Financial headline classification |
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- `xbrl_extract_llama_3_1_8b_4bits_r4` - XBRL tag extraction |
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- `xbrl_term_llama_3_1_8b_4bits_r4` - XBRL terminology processing |
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- `financebench_llama_3_1_8b_4bits_r4` - Comprehensive financial benchmark |
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- `finer_llama_3_1_8b_4bits_r4` - Financial NER |
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- `formula_llama_3_1_8b_4bits_r4` - Financial formula processing |
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## Quick Start |
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### 1. Installation |
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```bash |
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# Install dependencies |
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pip install -r requirements.txt |
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``` |
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### 2. Basic Usage |
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```python |
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from inference import FinLoRAPredictor |
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# Initialize predictor with 8-bit model (recommended) |
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predictor = FinLoRAPredictor( |
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model_name="sentiment_llama_3_1_8b_8bits_r8", |
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use_4bit=False |
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) |
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# Financial sentiment analysis |
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sentiment = predictor.classify_sentiment( |
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"The company's quarterly earnings exceeded expectations by 20%." |
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) |
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print(f"Sentiment: {sentiment}") |
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# Entity extraction |
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entities = predictor.extract_entities( |
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"Apple Inc. reported revenue of $394.3 billion in 2022." |
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) |
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print(f"Entities: {entities}") |
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# Use 4-bit model for memory efficiency (if you have limited GPU memory) |
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predictor_4bit = FinLoRAPredictor( |
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model_name="sentiment_llama_3_1_8b_4bits_r4", |
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use_4bit=True |
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) |
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# CPU-only mode (if no GPU available) |
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predictor_cpu = FinLoRAPredictor( |
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model_name="sentiment_llama_3_1_8b_8bits_r8", |
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use_4bit=False |
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) |
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# The script will automatically detect CPU and adjust accordingly |
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``` |
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### 3. Run Complete Test |
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```bash |
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# Test all models (this will download the base Llama model if not present) |
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python inference.py |
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# Test specific model |
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python -c " |
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from inference import FinLoRAPredictor |
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predictor = FinLoRAPredictor('sentiment_llama_3_1_8b_8bits_r8') |
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print('Model loaded successfully!') |
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" |
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``` |
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## Usage Examples |
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### Financial Sentiment Analysis |
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```python |
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predictor = FinLoRAPredictor("sentiment_llama_3_1_8b_8bits_r8") |
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# Test cases |
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test_texts = [ |
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"Stock prices are soaring to new heights.", |
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"Revenue declined by 15% this quarter.", |
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"The company maintained stable performance." |
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] |
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for text in test_texts: |
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sentiment = predictor.classify_sentiment(text) |
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print(f"Text: {text}") |
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print(f"Sentiment: {sentiment}\n") |
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``` |
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### Named Entity Recognition |
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```python |
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predictor = FinLoRAPredictor("ner_llama_3_1_8b_8bits_r8") |
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text = "Apple Inc. reported revenue of $394.3 billion in 2022." |
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entities = predictor.extract_entities(text) |
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print(f"Entities: {entities}") |
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``` |
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### XBRL Processing |
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```python |
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predictor = FinLoRAPredictor("xbrl_extract_llama_3_1_8b_8bits_r8") |
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text = "Total assets: $1,234,567,890. Current assets: $456,789,123." |
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xbrl_tags = predictor.extract_xbrl_tags(text) |
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print(f"XBRL Tags: {xbrl_tags}") |
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``` |
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### RAG-Enhanced Models |
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```python |
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# CFA RAG-enhanced model for financial knowledge |
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predictor = FinLoRAPredictor("cfa_rag_llama_3_1_8b_8bits_r8") |
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# Enhanced financial analysis with CFA knowledge |
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response = predictor.generate_response( |
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"Explain the concept of discounted cash flow valuation" |
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) |
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print(f"CFA Response: {response}") |
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# FinTagging RAG models for financial information extraction |
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fintagging_predictor = FinLoRAPredictor("fintagging_combined_rag_llama_3_1_8b_8bits_r8") |
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# Extract financial information with enhanced context |
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entities = fintagging_predictor.extract_entities( |
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"Apple Inc. reported revenue of $394.3 billion in 2022." |
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) |
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print(f"Enhanced Entities: {entities}") |
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``` |
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### Memory-Efficient 4-bit Models |
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```python |
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# For users with limited GPU memory |
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predictor = FinLoRAPredictor( |
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model_name="sentiment_llama_3_1_8b_4bits_r4", |
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use_4bit=True |
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) |
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# Same API as 8-bit models |
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sentiment = predictor.classify_sentiment("The market is performing well.") |
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``` |
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## Evaluation |
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### For Competition Organizers |
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This section provides guidance for evaluating the submitted models: |
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#### 1. Quick Model Test |
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```bash |
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# Test if all models can be loaded successfully |
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python test_submission.py |
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``` |
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#### 2. Comprehensive Evaluation |
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```bash |
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# Run full evaluation on all models and datasets |
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python comprehensive_evaluation.py |
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# Check results |
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cat comprehensive_evaluation_results.json |
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``` |
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#### 3. Incremental Evaluation |
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```bash |
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# Run evaluation on missing tasks |
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python incremental_evaluation.py |
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# Check results |
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cat incremental_evaluation_results.json |
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``` |
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#### 4. Evaluation Results |
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The evaluation results are provided in: |
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- `comprehensive_evaluation_results.json` - Complete evaluation results |
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- `incremental_evaluation_results.json` - Missing task evaluation results |
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#### 5. Model Performance Summary |
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All models have been evaluated on multiple financial datasets. See the Performance Results section below for detailed metrics. |
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### For Researchers |
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Run comprehensive evaluation on financial datasets: |
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```bash |
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# Run full evaluation |
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python comprehensive_evaluation.py |
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# Run incremental evaluation |
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python incremental_evaluation.py |
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# Run robust evaluation |
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python robust_incremental.py |
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``` |
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## Performance Results |
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The models have been evaluated on multiple financial datasets: |
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| Task | Dataset | F1 Score | Accuracy | |
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|------|---------|----------|----------| |
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| Sentiment Analysis | Financial Phrasebank | 0.333 | 0.500 | |
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| NER | Financial NER | 0.889 | 0.800 | |
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| Classification | Headline Classification | 0.697 | 0.700 | |
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| XBRL Processing | XBRL Tag Extraction | - | 0.200 | |
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| Sentiment Analysis | FIQA SA | 0.727 | 0.700 | |
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## Project Structure |
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``` |
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finlora_hf_submission/ |
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├── models/ # 8-bit LoRA model adapters (13 models) |
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│ ├── sentiment_llama_3_1_8b_8bits_r8/ |
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│ ├── ner_llama_3_1_8b_8bits_r8/ |
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│ ├── headline_llama_3_1_8b_8bits_r8/ |
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│ ├── xbrl_extract_llama_3_1_8b_8bits_r8/ |
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│ ├── xbrl_term_llama_3_1_8b_8bits_r8/ |
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│ ├── financebench_llama_3_1_8b_8bits_r8/ |
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│ ├── finer_llama_3_1_8b_8bits_r8/ |
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│ ├── formula_llama_3_1_8b_8bits_r8/ |
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│ ├── cfa_rag_llama_3_1_8b_8bits_r8/ # NEW: CFA RAG model |
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│ ├── fintagging_combined_rag_llama_3_1_8b_8bits_r8/ # NEW: Combined RAG |
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│ ├── fintagging_fincl_rag_llama_3_1_8b_8bits_r8/ # NEW: FinCL RAG |
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│ ├── fintagging_finni_rag_llama_3_1_8b_8bits_r8/ # NEW: FinNI RAG |
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│ └── xbrl_train.jsonl-meta-llama-Llama-3.1-8B-Instruct-8bits_r8/ |
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├── models_4bit/ # 4-bit LoRA model adapters (8 models) |
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│ ├── sentiment_llama_3_1_8b_4bits_r4/ |
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│ ├── ner_llama_3_1_8b_4bits_r4/ |
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│ ├── headline_llama_3_1_8b_4bits_r4/ |
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│ ├── xbrl_extract_llama_3_1_8b_4bits_r4/ |
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│ ├── xbrl_term_llama_3_1_8b_4bits_r4/ |
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│ ├── financebench_llama_3_1_8b_4bits_r4/ |
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│ ├── finer_llama_3_1_8b_4bits_r4/ |
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│ └── formula_llama_3_1_8b_4bits_r4/ |
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├── testdata/ # Evaluation datasets |
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│ ├── FinCL-eval-subset.csv |
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│ └── FinNI-eval-subset.csv |
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├── rag_system/ # RAG system components |
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├── inference.py # Main inference script |
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├── comprehensive_evaluation.py # Full evaluation script |
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├── incremental_evaluation.py # Incremental evaluation |
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├── robust_incremental.py # Robust evaluation |
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├── missing_tests.py # Missing test detection |
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├── requirements.txt # Python dependencies |
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└── README.md # This file |
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``` |
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## Environment Requirements |
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### Minimum Requirements (CPU Mode) |
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- Python 3.8+ |
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- PyTorch 2.0+ |
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- 8GB RAM |
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- No GPU required |
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### Recommended Requirements (GPU Mode) |
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- Python 3.9+ |
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- PyTorch 2.1+ |
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- CUDA 11.8+ (for NVIDIA GPUs) |
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- 16GB+ GPU memory |
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- 32GB+ RAM |
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### Installation Instructions |
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```bash |
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# 1. Clone or download this repository |
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# 2. Install dependencies |
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pip install -r requirements.txt |
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# 3. For GPU support (optional but recommended) |
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 |
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# 4. Verify installation |
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python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')" |
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``` |
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### Troubleshooting |
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**If you encounter memory issues:** |
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- Use 4-bit models instead of 8-bit models |
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- Reduce batch size in inference |
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- Use CPU mode if GPU memory is insufficient |
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**If models fail to load:** |
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- Ensure all model files are present in the correct directories |
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- Check that the base model (Llama-3.1-8B-Instruct) can be downloaded from HuggingFace |
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- Verify internet connection for model downloads |
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**Important Notes for Competition Organizers:** |
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- The base model (Llama-3.1-8B-Instruct) will be automatically downloaded from HuggingFace on first use (~15GB) |
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- All LoRA adapters are included in this submission and do not require additional downloads |
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- Models work in both CPU and GPU modes, with automatic device detection |
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- RAG-enhanced models require the same base model as regular models |
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## Model Details |
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### Training Configuration |
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- **LoRA Rank**: 8 |
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- **LoRA Alpha**: 16 |
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- **Learning Rate**: 1e-4 |
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- **Batch Size**: 4 |
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- **Epochs**: 3-5 |
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- **Quantization**: 8-bit (BitsAndBytes) / 4-bit (NF4) |
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### Training Data |
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- Financial Phrasebank |
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- FinGPT datasets (NER, Headline, XBRL) |
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- BloombergGPT financial datasets |
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- Custom financial text datasets |
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## Citation |
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If you use this work in your research, please cite: |
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```bibtex |
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@article{finlora2024, |
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title={FinLoRA: Financial Large Language Models with LoRA Adaptation}, |
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author={Your Name}, |
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journal={Financial AI Conference}, |
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year={2024} |
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} |
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``` |
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## License |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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## Contributing |
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Contributions are welcome! Please feel free to submit a Pull Request. |
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## Contact |
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For questions and support, please open an issue or contact [your-email@example.com](mailto:your-email@example.com). |
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## Submission Summary |
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### What's Included |
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- **21 Total Models**: 13 8-bit models (9 original + 4 RAG-enhanced) + 8 4-bit models |
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- **Complete Evaluation Results**: Comprehensive and incremental evaluation results |
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- **RAG-Enhanced Models**: CFA and FinTagging models with enhanced knowledge |
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- **Cross-Platform Support**: Works on CPU, GPU, and various memory configurations |
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- **Ready-to-Use**: All dependencies specified, automatic device detection |
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### Quick Start for Competition Organizers |
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1. Install dependencies: `pip install -r requirements.txt` |
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2. Test submission: `python test_submission.py` |
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3. Run evaluation: `python comprehensive_evaluation.py` |
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4. Check results: `cat comprehensive_evaluation_results.json` |
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### Model Categories |
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- **Financial NLP**: Sentiment, NER, Classification, XBRL processing |
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- **RAG-Enhanced**: CFA knowledge and FinTagging with retrieval augmentation |
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- **Memory Options**: Both 8-bit and 4-bit quantized versions available |
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## Acknowledgments |
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- Meta for the Llama-3.1-8B-Instruct base model |
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- Hugging Face for the transformers and PEFT libraries |
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- The financial NLP community for datasets and benchmarks |
