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Kamisori-daijin/textrm-28M-bizmail

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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textrm-28M-bizmail

A 28.19M parameter Transformer-based model that generates surprisingly coherent business-style emails.

Github: https://github.com/kamisori-daijin/textrm

v1.5 is Here : https://huggingface.co/Kamisori-daijin/textrm1.5-25M-bizmail

Overview

This project explores how far a small (~28M parameter) model can go in generating structured business email text.

The model is not fully instruction-following and may produce inconsistent or mixed outputs, but it can often generate realistic email-like text.

Features

  • —Small size (~28M parameters)
  • —Generates business-style email text
  • —Works best with simple prompts
  • —Occasionally produces surprisingly coherent outputs

Limitations

  • —Weak instruction following
  • —May mix multiple prompts or contexts
  • —Inconsistent tone and intent
  • —Not suitable for production use

Example

Prompt: Write a polite refusal email

Output: Write a polite refusal email and the company's well. Regarding [Company Name]'s AI-driven shift in [Project Name], I was awarded the [Award Name] for [Company Name] during this event. I was experiencing some unprecedented challenges and requires immediate attention. During this event, we've identified and updated the report. We have identified [brief, 1-2 key areas of feedback - e.g., increased customer development, lead our focus on [brief, neutral reason - e.g., 24-48 hours].

We’ve reviewed the updated prototype, and I need a concise and detailed explanation of the revised prototype by [Date]. We can discuss this further and explore a comprehensive approach to your clients.

Training

  • —Architecture: TRM (custom Transformer variant)
  • —Parameters: 28.19M
  • —Dataset: Synthetic business email dataset (generated using Gemma3-4B) Link: https://huggingface.co/datasets/Kamisori-daijin/email-datasets-20k
  • —Training epochs: 15

Usage

1.

bash
git clone https://github.com/kamisori-daijin/textrm.git
  1. 1.clone this repo
  1. 1.Move the cloned final_model.safetensors to the textrm folder.
  1. 1.
bash
cd textrm

python -m venv .venv

source .venv/bin/activate

pip install -r requirements.txt

python inference.py

Notes

This model is intended for research and experimentation purposes only.

License

Apache2

Disclaimer

This model was trained on synthetic data generated using Gemma3-4B (Google). This project is independent and does not replicate or fine-tune Gemma3-4B.