llk010502/llama3.1-8B-financial_sentiment

Model Card for Llama-3.1-8B Fine-Tuned for Financial Sentiment Analysis

This model is a fine-tuned version of Meta's Llama-3.1-8B, tailored for financial sentiment analysis tasks. It leverages LoRA and 8-bit quantization techniques to achieve efficient performance while reducing computational overhead.

Model Details

Model Description

• Model type: Causal Language Model fine-tuned for financial sentiment analysis
• Language(s): English
• Finetuned from model: meta-llama/Llama-3.1-8B

Direct Use

The model can be directly used for financial sentiment analysis tasks, including: - Analyzing financial news sentiment - Sentiment classification on financial social media data

How to Get Started with the Model

Use the following code to load the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Base and fine-tuned model
base_model = "meta-llama/Llama-3.1-8B"
peft_model = "llk010502/llama3.1-8B-financial_sentiment"

# Load the base model
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    trust_remote_code=True,
    device_map="auto"
)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

# Load the fine-tuned model
model = PeftModel.from_pretrained(model, peft_model)
model = model.eval()