Instructions to use Rajveerx11/email-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rajveerx11/email-classifier with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rajveerx11/email-classifier",
	filename="Phi-3-mini-4k-instruct-Q5_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Rajveerx11/email-classifier with llama.cpp:

Install from brew

brew 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_M

Install 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_M

Use 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_M

Build 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_M

Use Docker

docker model run hf.co/Rajveerx11/email-classifier:Q5_K_M

LM Studio
Jan
Ollama
How to use Rajveerx11/email-classifier with Ollama:
```
ollama run hf.co/Rajveerx11/email-classifier:Q5_K_M
```

Unsloth Studio

How to use Rajveerx11/email-classifier 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 Rajveerx11/email-classifier 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 Rajveerx11/email-classifier to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rajveerx11/email-classifier to start chatting

Docker Model Runner
How to use Rajveerx11/email-classifier with Docker Model Runner:
```
docker model run hf.co/Rajveerx11/email-classifier:Q5_K_M
```

Lemonade

How to use Rajveerx11/email-classifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rajveerx11/email-classifier:Q5_K_M

Run and chat with the model

lemonade run user.email-classifier-Q5_K_M

List all available models

lemonade list

email-classifier / README.md

Rajveerx11

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	---
	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

	```bash
	./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

	---