Quark-Mod-v0.1

A 135M parameter content moderation model fine-tuned from Quark-135M

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📋 Model Overview

Property	Value
Full name	Quark-Mod-v0.1
Base model	Quark-135M (pretrained from scratch)
Architecture	Decoder-only, GQA (9:3), SwiGLU, RoPE, RMSNorm
Parameters	135M
Context length	2048 tokens
Task	Multi-label content moderation (9 classes)
Language	English (v0.1)

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🎯 Intended Use

This model is designed to classify toxic and harmful content across 9 categories. It is intended for:

• ✅ Social media moderation
• ✅ Comment filtering systems
• ✅ Content safety pipelines
• ✅ Research on efficient moderation models

Limitations

• ⚠️ English only (v0.1)
• ⚠️ May struggle with subtle sarcasm or highly contextual toxicity
• ⚠️ Lower performance on rare classes due to dataset imbalance
• ⚠️ Not recommended for high-stakes decisions without human review

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🏷️ Labels (9 classes)

Label	Description	Training examples
toxic	General toxic content	32,263 (19.4%)
severe_toxic	Severe toxicity	1,423 (0.9%)
obscene	Obscene/profane language	7,567 (4.6%)
threat	Direct threats	445 (0.3%)
insult	Insulting content	7,065 (4.3%)
identity_hate	Hate targeting identity	1,263 (0.8%)
hate_speech	Explicit hate speech	1,265 (0.8%)
offensive	Offensive language	17,274 (10.4%)

Note: Multi-label classification — multiple classes can be active simultaneously.

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📊 Evaluation Results

Validation set: 18,436 examples

Class	F1 Score
toxic	0.909
offensive	0.938
obscene	0.796
insult	0.721
severe_toxic	0.498
identity_hate	0.415
hate_speech	0.319
threat	0.372

Metric	Score
Macro F1	0.552
Validation Loss	0.037

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🚀 Usage Example

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load model and tokenizer
model_name = "ThingsAI/Quark-Mod-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Labels
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", 
          "identity_hate", "hate_speech", "offensive"]

# Predict
def moderate(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048)
    with torch.no_grad():
        outputs = model(**inputs)
    predictions = (outputs.logits > 0).int()[0]
    detected = [labels[i] for i, v in enumerate(predictions) if v == 1]
    return detected if detected else ["clean"]

# Test
print(moderate("I love this community!"))        # ['clean']
print(moderate("You are an idiot and should die")) # ['toxic', 'insult']
print(moderate("Nice post, thanks for sharing")) # ['clean']