Fine-Tuned Fintech DistilGPT2

A DistilGPT2 model fine-tuned for fintech question-answering.

Load

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "TensorVizion/RAG-distilgpt2-turbo"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)

Model Summary

Provide a brief overview of the model including details about its architecture, how it can be used, characteristics of the model, training data, and evaluation results.

Usage

Sentence Transformer

Implementation requirements

Can be run by most pcs, 2gb of system ram can run this model locally

Model Characteristics

Small. weighing in at only 312mb this model can be run locally by most people.

Demographic groups

Worldwide

Evaluation data

What was the train / test / dev split? Are there notable differences between training and test data?

Evaluation Results