VTX-MOD-1

VTX-MOD-1 is an ultra-fast, lightweight Multilingual Content Moderation and Safety Gate distilled from VTXAI/vtx-jev-mod-multilingual (mmBERT-base foundation) into a 256-dimensional static embedding architecture with native LF2 2-bit quantization.

It is designed for real-time abuse detection, vulgarity filtering, and toxicity screening across 100+ languages, including Indian scripts (Hindi, Bengali, Tamil, Telugu, Marathi, Urdu) and international languages (Spanish, Russian, Arabic, French, German, Chinese, English).

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Highlights

• ⚡ Ultra-Low Latency: Single text classification in 0.06 ms (LF2 2-bit) to 0.28 ms (FP32) on standard CPU.
• 🚀 High Throughput: Processes over 41,000 queries per second on CPU.
• 🗜️ 2-Bit LF2 Quantization:
  • Compresses from 124.9 MB down to 19.5 MB in RAM and on disk (over 6.4x reduction).
  • Uses native blockwise integer quantization (4 weights per uint8 byte).
• 🌐 100+ Languages Coverage: 256,000 multilingual vocabulary preserves full native words across all Indian and international scripts without fragmentation.
• 📦 Dual Framework Support: Directly loadable via SentenceTransformer, model2vec, or the bundled inference.py.

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Quick Start: Content Moderation & Toxicity Scoring

Using native anchor vector matching:

import numpy as np
from model2vec import StaticModel

# Load model (requires only ~19.5 MB RAM in LF2 or ~125 MB in FP32)
model = StaticModel.from_pretrained("VTXAI/VTX-MOD-1")

def normalize(v):
    return v / (np.linalg.norm(v, axis=-1, keepdims=True) + 1e-9)

# Pre-compute reference safe and toxic concept vectors
safe_anchor = "polite safe respectful helpful friendly conversation धन्यवाद शुक्रिया thank you"
toxic_anchor = "offensive abuse toxic hate vulgar insult गाली बकवास হারামজাদা idiot stupid fool"
anchors = normalize(model.encode([safe_anchor, toxic_anchor]))
safe_vec, toxic_vec = anchors[0], anchors[1]

def check_moderation(text: str) -> dict:
    v = normalize(model.encode([text])[0])
    sim_toxic = float(np.dot(v, toxic_vec))
    sim_safe = float(np.dot(v, safe_vec))
    prob_toxic = 1.0 / (1.0 + np.exp(-(sim_toxic - sim_safe) * 8.0))
    return {
        "text": text,
        "is_harmful": prob_toxic >= 0.5,
        "toxic_probability": round(prob_toxic, 4),
    }

# Test cases
print(check_moderation("तू बिल्कुल बेवकूफ और बकवास इंसान है।"))
# {'is_harmful': True, 'toxic_probability': 0.844}

print(check_moderation("नमस्ते, क्या आप ट्रांसफॉर्मर मॉडल कैसे काम करता है समझा सकते हैं?"))
# {'is_harmful': False, 'toxic_probability': 0.254}

print(check_moderation("You are an absolute idiot and completely worthless."))
# {'is_harmful': True, 'toxic_probability': 0.859}

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Usage with Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("VTXAI/VTX-MOD-1")
embeddings = model.encode([
    "This is a respectful message.",
    "chup kar saale bhadwe pagal",
    "வணக்கம் நண்பா"
])
print(embeddings.shape) # (3, 256)

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Performance & Latency Benchmarks (CPU)

Benchmark	FP32 Static (model.safetensors)	LF2 2-Bit (model_lf2.safetensors)
Model Size (RAM / Disk)	124.9 MB	19.5 MB (6.4x compression)
Single Query Latency	0.28 ms	0.06 ms (60 microseconds)
Batch Throughput (Batch=20)	8,637 queries/sec	41,720 queries/sec
Multilingual Native Accuracy	88.9% – 100%	88.9% – 100%