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mistral_7b_finance_finetuned

Text Generation
Transformers
Safetensors
English
mistral
trl
sft
text-generation-inference
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metadata
library_name: transformers
tags:
  - trl
  - sft
license: mit
datasets:
  - gbharti/finance-alpaca
base_model:
  - mistralai/Mistral-7B-v0.1
language:
  - en

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