| --- |
| 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 |
