Model Description
This model is based on the Mistral 7B architecture, PEFT fine-tuned on financial data. It is designed to handle various finance-related NLP tasks such as financial text analysis, sentiment detection, market trend analysis, and more. This model leverages the powerful transformer architecture of Mistral with specialized fine-tuning for financial applications.
- Developed by: Cole McIntosh
- Model type: Transformer-based large language model (LLM)
- Language(s) (NLP): English
- Finetuned from model: Mistral 7B
Uses
Direct Use
The Mistral 7B Finance Fine-tuned model is designed to assist users with finance-related natural language processing tasks such as:
- Financial report analysis
- Sentiment analysis of financial news
- Forecasting market trends based on textual data
- Analyzing earnings call transcripts
- Extracting structured information from unstructured financial text
Downstream Use
This model can be fine-tuned further for more specific tasks such as:
- Portfolio analysis based on sentiment scores
- Predictive analysis for stock market movements
- Automated financial report generation
Out-of-Scope Use
This model should not be used for tasks unrelated to finance or those requiring a high level of factual accuracy in non-financial domains. It is not suitable for:
- Medical or legal document analysis
- General conversational chatbots (as the model may provide misleading financial interpretations)
- Decision-making without human oversight, especially in high-stakes financial operations
Recommendations
- Carefully review model outputs, especially in critical financial decisions.
- Use up-to-date fine-tuning datasets to ensure relevance.
- Cross-validate the model's predictions or insights with alternative data sources or human expertise.
How to Get Started with the Model
You can use the Hugging Face transformers
library to load and use this model. Here’s a basic example:
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("colesmcintosh/mistral_7b_finance_finetuned")
model = AutoModelForCausalLM.from_pretrained("colesmcintosh/mistral_7b_finance_finetuned")
# Example usage
inputs = tokenizer("Analyze the financial outlook for Q3 2024.", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
Training Details
Training Procedure
The fine-tuning process used PEFT to accelerate training on GPUs.
Summary
The model performs well in finance-specific tasks like sentiment analysis and entity recognition. It demonstrates strong generalization across different sectors but shows slight performance drops when analyzing non-English financial texts.
Model Architecture and Objective
The model is based on the Mistral 7B architecture, a highly optimized transformer-based model. Its primary objective is text generation and understanding, with a focus on financial texts.
Compute Infrastructure
Hardware
The model was fine-tuned using:
- 1 NVIDIA A100 GPU (40 GB)
Software
- Hugging Face
transformers
library - PEFT finetuning
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Model tree for colesmcintosh/mistral_7b_finance_finetuned
Base model
mistralai/Mistral-7B-v0.1