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

Libraries

How to use AshankGupta/tinyllama-email-reply with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AshankGupta/tinyllama-email-reply",
	filename="tinyllama-chat.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AshankGupta/tinyllama-email-reply with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshankGupta/tinyllama-email-reply:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AshankGupta/tinyllama-email-reply:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshankGupta/tinyllama-email-reply:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AshankGupta/tinyllama-email-reply:Q4_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 AshankGupta/tinyllama-email-reply:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AshankGupta/tinyllama-email-reply:Q4_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 AshankGupta/tinyllama-email-reply:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AshankGupta/tinyllama-email-reply:Q4_K_M

Use Docker

docker model run hf.co/AshankGupta/tinyllama-email-reply:Q4_K_M

LM Studio
Jan

vLLM

How to use AshankGupta/tinyllama-email-reply with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AshankGupta/tinyllama-email-reply:Q4_K_M

Ollama
How to use AshankGupta/tinyllama-email-reply with Ollama:
```
ollama run hf.co/AshankGupta/tinyllama-email-reply:Q4_K_M
```

Unsloth Studio new

How to use AshankGupta/tinyllama-email-reply 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 AshankGupta/tinyllama-email-reply 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 AshankGupta/tinyllama-email-reply to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use AshankGupta/tinyllama-email-reply with Docker Model Runner:
```
docker model run hf.co/AshankGupta/tinyllama-email-reply:Q4_K_M
```

Lemonade

How to use AshankGupta/tinyllama-email-reply with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AshankGupta/tinyllama-email-reply:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-email-reply-Q4_K_M

List all available models

lemonade list

TinyLlama Email Reply Generator

A fine-tuned TinyLlama model for generating professional email replies from incoming emails.

The adapter was trained on the Enron Email Reply Dataset to learn professional communication patterns.

Model Overview

Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Format: GGUF (Q4_K_M quantization)
Size: ~667 MB
Task: Email reply generation
Language: English

Quick Start with GGUF

Using Ollama (Recommended)

# 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

# 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

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

Downloads last month: 3

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for AshankGupta/tinyllama-email-reply

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Quantized

(146)

this model