How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rajveerx11/email-classifier:Q5_K_M# Run inference directly in the terminal:
llama-cli -hf Rajveerx11/email-classifier:Q5_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rajveerx11/email-classifier:Q5_K_M# Run inference directly in the terminal:
./llama-cli -hf Rajveerx11/email-classifier:Q5_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rajveerx11/email-classifier:Q5_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rajveerx11/email-classifier:Q5_K_MUse Docker
docker model run hf.co/Rajveerx11/email-classifier:Q5_K_MQuick Links
📧 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
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Hardware compatibility
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5-bit
Model tree for Rajveerx11/email-classifier
Base model
microsoft/Phi-3-mini-4k-instruct
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Rajveerx11/email-classifier:Q5_K_M# Run inference directly in the terminal: llama-cli -hf Rajveerx11/email-classifier:Q5_K_M