Instructions to use nosadaniel/mistral-7b-instruct-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nosadaniel/mistral-7b-instruct-tuned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "nosadaniel/mistral-7b-instruct-tuned")

Transformers

How to use nosadaniel/mistral-7b-instruct-tuned with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nosadaniel/mistral-7b-instruct-tuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nosadaniel/mistral-7b-instruct-tuned", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nosadaniel/mistral-7b-instruct-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nosadaniel/mistral-7b-instruct-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nosadaniel/mistral-7b-instruct-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nosadaniel/mistral-7b-instruct-tuned

SGLang

How to use nosadaniel/mistral-7b-instruct-tuned with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nosadaniel/mistral-7b-instruct-tuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nosadaniel/mistral-7b-instruct-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nosadaniel/mistral-7b-instruct-tuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nosadaniel/mistral-7b-instruct-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use nosadaniel/mistral-7b-instruct-tuned with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nosadaniel/mistral-7b-instruct-tuned to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nosadaniel/mistral-7b-instruct-tuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nosadaniel/mistral-7b-instruct-tuned to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nosadaniel/mistral-7b-instruct-tuned",
    max_seq_length=2048,
)

Docker Model Runner
How to use nosadaniel/mistral-7b-instruct-tuned with Docker Model Runner:
```
docker model run hf.co/nosadaniel/mistral-7b-instruct-tuned
```

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

Install Ollama – https://ollama.com

Pull the base model:

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

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

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

Then run:

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

Run the merged model:
```
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:

@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

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Model tree for nosadaniel/mistral-7b-instruct-tuned

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

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

Adapter

(124)

this model