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nosadaniel/mistral-7b-instruct-tuned

sourceHugging Faceupdated 10mo agoView on Hugging Face

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Model Card

Model Card for mistral-7b-instruct-sft

Abstract

This repository hosts a fine‑tuned Mistral 7B‑Instruct model that leverages parameter‑efficient LoRA 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 (94.9 % accuracy, 93.9 % precision, 96.1 % recall).

Model Details

Model Description

—Developed by: Montimage
—Model type: Large Language Model (LLM)
—Language(s): English
—License: Apache‑2.0
—Finetuned from model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit

Model Sources

—Repository: https://huggingface.co/nosadaniel/mistral-7b-instruct-tuned

How to Get Started with the Model

You can download the model using the Hugging Face Space.

Locally using Ollama

1.Install Ollama – https://ollama.com
2.Pull the base model:

bash

   ollama pull unsloth/mistral-7b-instruct-v0.3-bnb-4bit

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

text

   FROM mistral-7b-instruct-v0.3-bnb-4bit
   ADAPTER /path/to/your/downloaded/adapter

Then run:

bash

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

1.Run the merged model:

bash

   ollama run merged-model

Training Details

Training Data

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

Training Procedure

—Fine‑tuning method: LoRA (Low‑Rank Adaptation) via Unsloth
—Training regime: fp16 mixed precision
—Epochs: 3 (full dataset)
—Learning rate: 2e‑4
—Batch size: 32

Model Comparison Table (selected row for this model)

Model	Samples	Accuracy	Precision	Recall	F1‑Score	Specificity	FPR	FNR	MCC	Validity	Avg Response Time (s)	Total Input Tokens	Total Output Tokens	Avg Input Tokens	Avg Output Tokens	Quality Mean	Quality Std	Excellent (%)	Good (%)	Fair (%)	Poor (%)
mistral-7b-instruct-sft	256	0.949	0.939	0.961	0.950	0.938	0.062	0.039	0.899	100.0 %	18.70	144,247	72,299	563.5	282.4	0.951	0.098	94.9	0.0	5.1	0.0
mistral:7b	256	0.840	0.939	0.727	0.819	0.953	0.047	0.273	0.698	100.0 %	12.57	144,247	76,797	563.5	300.0	0.850	0.160	84.0	0.0	6.6	9.4

Comparison with Base Model (mistral:7b): The fine‑tuned model achieves substantially higher accuracy (94.9 % vs 84.0 %), recall (96.1 % vs 72.7 %), and overall quality metrics, while maintaining comparable precision, demonstrating the effectiveness of LoRA fine‑tuning for email‑phishing detection.

Model Performance Analysis – mistral‑7b‑instruct‑sft

—Total Responses: 256
—Accuracy: 94.9 % (243/256)
—Valid Responses: 100 % (256/256)
—Average Confidence: 0.921

Classification Metrics

Metric	Value
Accuracy	94.9 %
Precision	93.9 %
Recall	96.1 %
F1‑Score	95.0 %
Specificity	93.8 %

Confusion Matrix

	Predicted Positive	Predicted Negative
Actual Positive	123 (TP)	5 (FN)
Actual Negative	8 (FP)	120 (TN)

Additional Metrics

—False Positive Rate: 6.2 %
—False Negative Rate: 3.9 %
—Negative Predictive Value: 96.0 %
—Matthews Correlation Coefficient: 0.899

Performance Insights

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

Citation

BibTeX:

bibtex

@dataset{mistral-7b-instruct-sft,
  title={Fine‑tuned Mistral 7B‑Instruct model for Email Phishing Detection},
  author={Montimage, Nosadaniel, Luong89},
  year={2025},
  publisher={Montimage}
}

APA:

Montimage, Nosakhare Daniel Ahanor, & Luong89. (2025). Fine‑tuned Mistral 7B‑Instruct model for Email Phishing Detection. Montimage.

Model Card Authors

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

Framework versions

—PEFT 0.17.1