llama3-1-8b-tuned

Model Card for llama3-1-8b-tuned

Abstract

This repository hosts a fine‑tuned Llama 3.1 8B model that leverages LoRA (Low‑Rank Adaptation) via the Unsloth framework. The model is adapted for email‑security tasks using a curated phishing‑email training dataset and demonstrates state‑of‑the‑art performance (93.4 % accuracy, 97.4 % precision, 89.1 % recall) compared to the base model.

Model Details

Model Description

• Developed by: Montimage
• Model type: Large Language Model (LLM)
• Language(s): English
• License: Apache‑2.0
• Finetuned from model: unsloth/llama‑3.1‑8b‑bnb‑4bit

Model Sources

• Repository: https://huggingface.co/nosadaniel/llama3-1-8b-tuned
• Demo: https://huggingface.co/spaces/nosadaniel/fined-model

How to Get Started with the Model

You can test the model using the Hugging Face Space.

Locally using Ollama

1. Install Ollama – https://ollama.com
2. Pull the base model (chooanse one):

   ollama pull unsloth/llama-3.1-8b-bnb-4bit
   # or
   ollama pull llama-3.1-8b
   

3. Create a merged‑model manifest (no file extension) with the following content and place it in a folder of your choice:

   FROM llama3.1:8b
   ADAPTER /path/to/your/downloaded/adapter
   

   Then run:

   ollama create merged-model --from ./merged-model
   

4. Run the merged model:

   ollama run merged-model
   

Training Details

Training Data

• Dataset: Phishing Email Training Dataset
• Link: https://huggingface.co/datasets/nosadaniel/phishing-email-training-dataset

Training Procedure

• Fine‑tuning method: LoRA via Unsloth
• Precision: fp16 mixed precision
• Epochs: 3 (full dataset)
• Learning rate: 2e‑4
• Batch size: 32

Model Comparison Table

Model Performance Analysis – llama3.1:8b_sft

• Total Responses: 256
• Accuracy: 93.4 % (239/256)
• Valid Responses: 100 % (256/256)
• Average Confidence: 0.927

Classification Metrics

Confusion Matrix

Additional Metrics

• False Positive Rate: 2.3 %
• False Negative Rate: 10.9 %
• Negative Predictive Value: 89.9 %
• Matthews Correlation Coefficient: 0.870

Performance Insights

• ✅ High Precision – Low false‑positive rate, fostering user trust.
• ✅ High Recall – Catches the vast majority of phishing attempts.
• ✅ Excellent F1‑Score – Well‑balanced precision and recall.
• ✅ Strong MCC – Strong overall correlation between predictions and ground truth.

Citation

BibTeX:

@dataset{llama3-1-8b-tuned,
  title={Fine‑tuned Llama 3.1 8B model for Email Phishing Detection},
  author={Montimage, Nosakhare Daniel Ahanor, Luong89},
  year={2025},
  publisher={Montimage}
}

Model Card Authors

Montimage Email Security Research Division AI/ML Engineering Team Cybersecurity Domain Experts

Framework versions

• PEFT 0.17.1