jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit
jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit is an 8 billion parameter Llama 3.1 model fine-tuned by jhon53 using QLoRA for 3-class financial sentiment analysis (negative, neutral, positive). This model excels at classifying financial sentiment, achieving significantly improved accuracy and Macro-F1 scores on both in-domain (FPB) and out-of-domain (FiQA-SA) datasets compared to its base model. It is specifically optimized for financial text classification tasks, leveraging a 32768 token context length.
Loading preview...
Llama 3.1 8B Financial Sentiment Model
This model, developed by jhon53, is a fine-tuned version of the Meta-Llama-3.1-8B-Instruct base model, specifically optimized for financial sentiment analysis. It utilizes QLoRA (4-bit NF4 frozen base with bf16 LoRA adapters) to efficiently achieve high performance on a specialized task.
Key Capabilities
- 3-Class Financial Sentiment Analysis: Accurately classifies financial text into
negative,neutral, orpositivesentiment categories. - Enhanced Performance: Demonstrates substantial improvements over the base Llama 3.1 8B model in financial sentiment tasks. For instance, it achieves 0.9748 accuracy on FPB in-domain data (up from 0.8908) and 0.9402 accuracy on FiQA-SA out-of-domain data (up from 0.8120).
- Efficient Fine-tuning: Fine-tuned using QLoRA on the
FinGPT/fingpt-sentiment-traindataset, making it a specialized and efficient solution for financial NLP.
Good for
- Financial Text Classification: Ideal for applications requiring precise sentiment analysis of financial news, reports, social media, or other textual data.
- Research and Development: Provides a strong baseline for further research into financial NLP and fine-tuning techniques on large language models.
- Resource-Efficient Deployment: The QLoRA fine-tuning approach allows for more efficient deployment and inference compared to full fine-tuning.