metadata
license: mit
language:
- en
base_model:
- microsoft/Phi-3-mini-4k-instruct
tags:
- gguf
- llama.cpp
- phi-3
pipeline_tag: text-classification
📧 Email Classifier (Phi-3 Mini Fine-Tuned GGUF)
🧠 Model Overview
This model is a fine-tuned version of Phi-3 Mini (4K Instruct) optimized for email classification tasks such as spam detection and categorization.
- Base Model: Phi-3 Mini 4K Instruct
- Fine-tuning: Supervised fine-tuning on email dataset
- Format: GGUF (for efficient local inference)
- Quantization: Q5_K_M
📚 Dataset
The model was trained on:
- Dataset:
json22322/email-classifier - Contains labeled email samples for classification tasks (e.g., spam vs non-spam)
🎯 Task
- Email classification
- Spam detection
- Priority categorization
⚙️ Usage
Using llama.cpp
./main -m Phi-3-mini-4k-instruct-Q5_K_M.gguf -p "Classify this email: Subject: ... Body: ..."
🧪 Example
Input:
Subject: Win a free iPhone now!!!
Body: Click here to claim your reward.
Output:
Spam
⚠️ Limitations
- Performance depends on dataset quality
- May not generalize to unseen domains
- Sensitive to prompt phrasing
📦 Files
| File | Description |
|---|---|
| *.gguf | Quantized fine-tuned model |
📌 Notes
- Optimized for local inference (CPU-friendly)
- Compatible with llama.cpp and GGUF runtimes
📜 License
Same as base model (Phi-3).
🙌 Acknowledgements
- Microsoft for Phi-3
- Hugging Face
- llama.cpp