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README.md

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----
-license: mit
-language:
-- en
-tags:
-- finance
-- nlp
-- lora
-- llama
-- sentiment-analysis
-- named-entity-recognition
-- xbrl
-- financial-analysis
-pipeline_tag: text-generation
----
-
-# FinLoRA: Financial Large Language Models with LoRA Adaptation
-
-## 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 CFA knowledge integration.
-
-## Model Architecture
-
-- **Base Model**: Meta-Llama-3.1-8B-Instruct
-- **Adaptation Method**: LoRA (Low-Rank Adaptation)
-- **Quantization**: 8-bit quantization for efficient inference
-- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge integration
-
-## Available Models
-
-### Core Financial Models
-- `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
-- `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
-
-## Quick Start
-
-### 1. Install Dependencies
-```bash
-pip install transformers torch peft bitsandbytes accelerate datasets
-```
-
-### 2. Load and Use a Model
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-from peft import PeftModel
-import torch
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-# Load model with quantization
-bnb_config = BitsAndBytesConfig(load_in_8bit=True)
-base_model = AutoModelForCausalLM.from_pretrained(
-    "meta-llama/Llama-3.1-8B-Instruct",
-    quantization_config=bnb_config,
-    device_map="auto"
-)
-
-# Load LoRA adapter
-model = PeftModel.from_pretrained(base_model, "models/sentiment_llama_3_1_8b_8bits_r8")
-
-# Generate prediction
-def predict(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Test
-result = predict("Classify sentiment: 'The stock market is performing well today.'")
-print(result)
-```
-
-### 3. Run Evaluation
-```bash
-python comprehensive_evaluation.py
-```
-
-### 4. Run Inference
-```bash
-python inference.py
-```
-
-## Performance
-
-The models have been evaluated on multiple financial datasets:
-
-- **Financial Phrasebank**: F1=0.333, Accuracy=0.500
-- **NER Classification**: F1=0.889, Accuracy=0.800
-- **Headline Classification**: F1=0.697, Accuracy=0.700
-- **XBRL Tag Extraction**: Accuracy=0.200
-- **FIQA Sentiment Analysis**: F1=0.727, Accuracy=0.700
-
-## Citation
-
-```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.