SpiceeChat/FirstName-Genre-Classifier-30M-SFT

<p align="center"> <img src="https://huggingface.co/spaces/SpiceeChat/README/resolve/main/SpiceeChatorglogo.png" alt="SpiceeChat" width="120"> </p>

<h1 align="center">FirstName Gender Classifier — 30M</h1>

<p align="center"> <em>Lightweight, fast, and accurate — because guessing isn't a strategy.</em> </p>

<p align="center"> <a href="https://huggingface.co/SpiceeChat"><img src="https://img.shields.io/badge/SpiceeChat-🔥-orange" alt="SpiceeChat"></a> <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-yellow" alt="License"></a> <img src="https://img.shields.io/badge/Params-~20M-blue" alt="Params"> <img src="https://img.shields.io/badge/Accuracy-84.7%25-green" alt="Accuracy"> </p>

Overview

This model is a fine-tuned version of a custom 20M-parameter CausalLM architecture, originally built by PhysiQuanty. It was trained on a combination of:

• —150,000 samples from the SpiceeChat/Genre-Classifier dataset
• —922 hand-curated examples to improve coverage and diversity

The result is a compact, production-ready classifier that predicts gender from a first name with ~85% accuracy and no unnecessary overhead.

Quick Start

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained(
    "SpiceeChat/FirstName-Genre-Classifier-30M-SFT",
    trust_remote_code=True   # custom architecture, audited and safe
)
tokenizer = AutoTokenizer.from_pretrained(
    "SpiceeChat/FirstName-Genre-Classifier-30M-SFT",
    trust_remote_code=True
)

name = "Arjun"
inputs = tokenizer(name, return_tensors="pt")
pred, probs = model.predict_gender(inputs.input_ids)
gender = "M" if pred.item() == 1 else "F"
print(f"{name} → {gender} (confidence: {probs.max().item():.2f})")

Expected output:

Arjun → M (confidence: 0.98)

Performance

Trained for 3 epochs with class weighting (F : M = 3:1) to handle the natural imbalance in the training data. Loss dropped cleanly from 0.41 to 0.34 across training — stable convergence, no overfitting.

What Makes This Model Different

• —Handles global names — from Wei (Chinese) to Haruto (Japanese) to Ama (Ghanaian)
• —Generalizes beyond dictionaries — learns naming patterns rather than relying on lookup tables
• —Custom lightweight architecture — small enough to run comfortably on CPU
• —Fully compatible with Hugging Face Transformers — loads like any standard model

Training Details

Notes

• —The model uses weight tying between head.weight and tok_emb.weight. A harmless head.weight | MISSING warning may appear on load — this is expected behavior.
• —trust_remote_code=True is required because the architecture is custom. The modeling code is included in this repository and fully auditable.

Try It Yourself

python -c "
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('SpiceeChat/FirstName-Genre-Classifier-30M-SFT', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('SpiceeChat/FirstName-Genre-Classifier-30M-SFT', trust_remote_code=True)
name = input('Enter a first name: ')
inputs = tokenizer(name, return_tensors='pt')
pred, _ = model.predict_gender(inputs.input_ids)
print('M' if pred.item() == 1 else 'F')
"

License

Released under the Apache 2.0 license. Use it, modify it, ship it — no strings attached.

<p align="center"> _{Built with a lot of caffeine ☕ by SpiceeChat} </p>

Built by PhysiQuanty(Did the most work) and QuantaSparkLabs.