The FinBen Team · Finance
FinMA
A financial large language model instruction-tuned on a comprehensive financial instruction dataset spanning multiple financial tasks.
Overview
FinMA is part of the PIXIU financial AI project and is an instruction-tuned LLM designed for broad financial task coverage. It is fine-tuned on a diverse instruction dataset covering financial sentiment analysis, named entity recognition, question answering, stock prediction, and credit scoring. FinMA aims to provide a single model capable of handling the wide spectrum of financial NLP tasks rather than requiring task-specific models.
Parameters
7B / 30B variants
Base Model
LLaMA
Training Data
PIXIU financial instruction dataset (136K samples)
Tasks Covered
Sentiment, NER, QA, prediction, credit scoring
License
Apache 2.0
Capabilities
Multi-task financial NLP
Financial sentiment analysis
Credit risk assessment
Financial question answering
Stock movement prediction
Use Cases
Building unified financial AI assistants that handle diverse queries
Automating credit scoring from financial documents
Providing financial Q&A for research analysts
Performing multi-task financial document analysis
Pros
- +Covers a broad range of financial NLP tasks in one model
- +Open-source with comprehensive evaluation benchmark (PIXIU)
- +Multiple model sizes for different deployment needs
- +Strong instruction-following capabilities for financial queries
Cons
- -Jack-of-all-trades may underperform task-specific models
- -Training data size is limited compared to BloombergGPT
- -Based on older LLaMA architecture
- -May require additional fine-tuning for production financial use
Pricing
Free and open-source. Self-hosting required. The 7B variant runs on consumer hardware; the 30B variant requires multi-GPU setup.