# Pull the model from Hugging Face
huggingface-cli download AshankGupta/tinyllama-email-reply tinyllama-chat.Q4_K_M.gguf --local-dir ./model

# Or download directly
curl -L -o model.gguf "https://huggingface.co/AshankGupta/tinyllama-email-reply/resolve/main/tinyllama-chat.Q4_K_M.gguf"

# Create Ollama model
ollama create tinyllama-email-reply -f Modelfile

# Run
ollama run tinyllama-email-reply

Using llama.cpp

bash

# Download GGUF file from Hugging Face
curl -L -o tinyllama-email-reply.Q4_K_M.gguf \
  "https://huggingface.co/AshankGupta/tinyllama-email-reply/resolve/main/tinyllama-chat.Q4_K_M.gguf"

# Run inference
./llama.cpp/llama-cli -m tinyllama-email-reply.Q4_K_M.gguf -p "Write a professional email reply to: Can you send the invoice by tomorrow?"

Using Python

python

from llama_cpp import Llama

model = Llama(
    model_path="tinyllama-chat.Q4_K_M.gguf",
    n_ctx=1024,
)

prompt = """You are an AI assistant that writes professional email replies.

Email:
Can you send the invoice by tomorrow?

Reply:"""

output = model(prompt, max_tokens=120)
print(output["choices"][0]["text"])

Intended Use

This model is designed for:

Email reply suggestion systems
AI productivity tools
Email assistants
Local AI workflows
Research on small language models

Training Dataset

The model was trained using the Enron Email Reply Dataset, which contains real-world corporate email conversations.

Dataset characteristics:

~15,000 email-reply pairs
Business and professional communication
Cleaned and formatted into instruction-style prompts

Training format example:

Instruction:
Generate a professional email reply.

Email:
Can you send the project report by tomorrow?

Reply:
Sure, I will send the report by tomorrow.

Training Details

Fine-tuning technique: LoRA
Training framework: Unsloth
Sequence length: 512 tokens
Optimizer: AdamW
Base architecture: TinyLlama 1.1B
Quantization: Q4_K_M

Example

Input Email

Hi,

Can you send the invoice by tomorrow?

Generated Reply

Sure, I will send the invoice by tomorrow.

Limitations

The model may produce generic replies.
Performance is limited by the small size of the base model.
It may occasionally generate repetitive outputs.
Not suitable for sensitive or confidential communications.

License

This model follows the license of the base model:

TinyLlama License

Please review the base model license before commercial usage.

Acknowledgements

TinyLlama team for the base model
Unsloth for efficient LoRA training
Enron Email Dataset for training data