---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: llama.cpp
pipeline_tag: text-generation
tags:
- tinyllama
- email-reply
- gguf
- ollama
- local-ai
license: mit
language:
- en
---

# 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)

```bash
# 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