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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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- apollo |
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- rag |
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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, **RAG-enhanced models** for CFA knowledge and FinTagging tasks, and **APOLLO reasoning layers** for advanced numerical calculations. |
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## Model Architecture |
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- **Base Model**: Meta-Llama-3.1-8B-Instruct (downloaded locally) |
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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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- **Multi-Layer Support**: RAG + APOLLO layered architecture |
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- **Local Usage**: All models run locally without requiring Hugging Face online access |
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- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge, FinTagging, numerical reasoning |
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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 (Knowledge-Augmented) |
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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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### APOLLO Models (Advanced Reasoning Layer) |
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- `apollo_cfa_rag_llama_3_1_8b_8bits_r8` - APOLLO reasoning layer for CFA tasks |
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- `apollo_fintagging_combined_llama_3_1_8b_8bits_r8` - APOLLO reasoning layer for FinTagging tasks |
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**Note**: APOLLO models are designed to be loaded on top of RAG models for enhanced numerical reasoning and calculation capabilities. |
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### Bloomberg-Enhanced Models (Specialized Financial Tasks) |
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- `finlora_lora_ckpt_llama_8bit_r8` - Bloomberg FPB and FIQA specialized model |
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- `finlora_heads_llama_8bit_r8.pt` - Bloomberg model weights (71MB) |
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**Note**: Bloomberg models are specialized for Financial Phrasebank (FPB) and Financial Question Answering (FIQA) tasks. |
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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. Local Model Setup |
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**Important**: This project uses locally downloaded models, not online Hugging Face models. |
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```bash |
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# The base Llama-3.1-8B-Instruct model will be automatically downloaded to local cache |
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# No internet connection required after initial setup |
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# All LoRA adapters are included in this repository |
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``` |
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### 3. 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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``` |
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### 4. 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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### APOLLO Models (Advanced Reasoning) |
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**Important**: APOLLO models are designed for advanced numerical reasoning and should be used for complex financial calculations. |
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```python |
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# Load APOLLO model for advanced reasoning |
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apollo_predictor = FinLoRAPredictor("apollo_cfa_rag_llama_3_1_8b_8bits_r8") |
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# Financial calculations and reasoning |
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calculation = apollo_predictor.generate_response( |
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"Calculate the present value of $10,000 received in 3 years with 5% annual discount rate" |
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) |
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print(f"APOLLO Calculation: {calculation}") |
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# Complex financial analysis |
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analysis = apollo_predictor.generate_response( |
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"Analyze the impact of a 2% interest rate increase on a 10-year bond with 3% coupon rate" |
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) |
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print(f"APOLLO Analysis: {analysis}") |
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# Formula processing |
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formula_result = apollo_predictor.generate_response( |
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"Solve: If a company has $1M revenue, 20% profit margin, and 10% growth rate, what's next year's profit?" |
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) |
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print(f"APOLLO Formula Result: {formula_result}") |
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``` |
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### Multi-Layer LoRA Architecture (RAG + APOLLO) |
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For maximum performance, you can combine RAG and APOLLO models: |
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```python |
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# Step 1: Load RAG model for knowledge retrieval |
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rag_predictor = FinLoRAPredictor("cfa_rag_llama_3_1_8b_8bits_r8") |
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# Step 2: Load APOLLO model for reasoning (this will be layered on top) |
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apollo_predictor = FinLoRAPredictor("apollo_cfa_rag_llama_3_1_8b_8bits_r8") |
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# Use for complex financial reasoning tasks |
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complex_query = """ |
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Given the following financial data: |
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- Revenue: $50M |
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- Cost of Goods Sold: $30M |
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- Operating Expenses: $15M |
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- Tax Rate: 25% |
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Calculate the net income and explain the calculation steps. |
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""" |
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response = apollo_predictor.generate_response(complex_query) |
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print(f"Multi-Layer Response: {response}") |
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``` |
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### Bloomberg-Enhanced Models (FPB & FIQA Specialized Tasks) |
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**Important**: Bloomberg models require special environment setup and are optimized for Financial Phrasebank (FPB) and Financial Question Answering (FIQA) tasks. |
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#### Environment Setup for Bloomberg Models |
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```bash |
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# 1. Create conda environment using the provided configuration |
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conda env create -f finlora_hf_submission/Bloomberg_fpb_and_fiqa/environment_contrasim.yml |
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# 2. Activate the environment |
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conda activate finenv |
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# 3. Navigate to the Bloomberg evaluation directory |
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cd finlora_hf_submission/Bloomberg_fpb_and_fiqa/ |
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``` |
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#### Testing Bloomberg Models on FPB and FIQA Datasets |
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```bash |
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# Run Bloomberg model evaluation |
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python trytry1.py |
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``` |
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**Configuration Notes for Testing:** |
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1. **Dataset Configuration**: In `trytry1.py`, modify the `EVAL_FILES` line: |
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```python |
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# Replace with your test datasets |
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EVAL_FILES = ["fiqa_test.jsonl", "fpb_test.jsonl"] |
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``` |
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2. **Model Path Configuration**: For local testing, update the `BASE_DIR` in `trytry1.py`: |
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```python |
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# For local Llama model deployment |
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BASE_DIR = "path/to/your/local/llama/model" |
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# For Hugging Face online model (original setting) |
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BASE_DIR = "d04e592bb4f6aa9cfee91e2e20afa771667e1d4b" |
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``` |
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3. **Model Components**: |
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- `ADAPTER_DIR`: Points to the LoRA adapter (`finlora_lora_ckpt_llama_8bit_r8`) |
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- `HEADS_PATH`: Points to the model weights (`finlora_heads_llama_8bit_r8.pt`) |
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#### Bloomberg Model Usage Example |
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```python |
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# Bloomberg models are specialized for FPB and FIQA tasks |
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# They provide enhanced performance on financial sentiment analysis |
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# and financial question answering compared to standard models |
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# The evaluation script automatically handles: |
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# - Model loading and configuration |
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# - Dataset processing |
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# - Performance metrics calculation |
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# - Memory management for large models |
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``` |
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## Local Model Management |
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### Model Storage |
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- **Base Model**: Downloaded to `~/.cache/huggingface/transformers/` |
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- **LoRA Adapters**: Stored in `models/` directory |
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- **No Online Dependency**: All models run locally after initial download |
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### Model Loading Process |
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1. **Base Model**: Automatically downloaded on first use (~15GB) |
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2. **LoRA Adapters**: Loaded from local `models/` directory |
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3. **Quantization**: Applied during loading (8-bit or 4-bit) |
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4. **Device Detection**: Automatically uses GPU if available, falls back to CPU |
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### Performance Optimization |
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```python |
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# For better performance on GPU |
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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 # Use 8-bit for better performance |
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) |
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# For memory-constrained environments |
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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 # Use 4-bit for memory efficiency |
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) |
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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. APOLLO Model Testing |
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```bash |
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# Test APOLLO reasoning capabilities |
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python -c " |
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from inference import FinLoRAPredictor |
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apollo = FinLoRAPredictor('apollo_cfa_rag_llama_3_1_8b_8bits_r8') |
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result = apollo.generate_response('Calculate 15% of $1000') |
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print(f'APOLLO Test: {result}') |
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" |
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``` |
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#### 5. Bloomberg Model Testing (FPB & FIQA) |
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```bash |
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# Setup Bloomberg environment |
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conda env create -f finlora_hf_submission/Bloomberg_fpb_and_fiqa/environment_contrasim.yml |
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conda activate finenv |
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# Navigate to Bloomberg evaluation directory |
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cd finlora_hf_submission/Bloomberg_fpb_and_fiqa/ |
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# Configure test datasets in trytry1.py: |
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# 1. Update EVAL_FILES = ["your_fiqa_test.jsonl", "your_fpb_test.jsonl"] |
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# 2. Update BASE_DIR for local model path or keep original for Hugging Face |
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# Run Bloomberg model evaluation |
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python trytry1.py |
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``` |
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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 (15 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/ # RAG-enhanced CFA model |
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│ ├── fintagging_combined_rag_llama_3_1_8b_8bits_r8/ # Combined RAG |
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│ ├── fintagging_fincl_rag_llama_3_1_8b_8bits_r8/ # FinCL RAG |
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│ ├── fintagging_finni_rag_llama_3_1_8b_8bits_r8/ # FinNI RAG |
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│ ├── apollo_cfa_rag_llama_3_1_8b_8bits_r8/ # APOLLO reasoning layer |
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│ ├── apollo_fintagging_combined_llama_3_1_8b_8bits_r8/ # APOLLO reasoning layer |
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│ └── xbrl_train.jsonl-meta-llama-Llama-3.1-8B-Instruct-8bits_r8/ |
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├── Bloomberg_fpb_and_fiqa/ # Bloomberg specialized models for FPB & FIQA |
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│ ├── finlora_heads_llama_8bit_r8.pt |
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│ ├── finlora_lora_ckpt_llama_8bit_r8/ |
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│ ├── environment_contrasim.yml # Conda environment configuration |
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│ └── trytry1.py # Bloomberg model evaluation script |
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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 initial model download |
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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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- APOLLO models provide enhanced reasoning capabilities for complex financial tasks |
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- All models run locally without requiring ongoing internet connection |
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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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- APOLLO reasoning datasets for numerical calculations |
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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 in the repository. |
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## Submission Summary |
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### What's Included |
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- **17 Total Models**: 15 8-bit models (9 original + 4 RAG-enhanced + 2 APOLLO) + 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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- **APOLLO Reasoning**: Advanced numerical reasoning and calculation capabilities |
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- **Cross-Platform Support**: Works on CPU, GPU, and various memory configurations |
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- **Local Execution**: All models run locally without online dependencies |
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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. Test APOLLO reasoning: `python -c "from inference import FinLoRAPredictor; apollo = FinLoRAPredictor('apollo_cfa_rag_llama_3_1_8b_8bits_r8'); print(apollo.generate_response('Calculate 10% of 500'))"` |
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5. Test Bloomberg models (FPB & FIQA): |
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```bash |
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conda env create -f finlora_hf_submission/Bloomberg_fpb_and_fiqa/environment_contrasim.yml |
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conda activate finenv |
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cd finlora_hf_submission/Bloomberg_fpb_and_fiqa/ |
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# Configure EVAL_FILES and BASE_DIR in trytry1.py |
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python trytry1.py |
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``` |
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6. 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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- **APOLLO Reasoning**: Advanced numerical calculations and financial reasoning |
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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 |
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- APOLLO reasoning framework for enhanced numerical capabilities |
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