---
license: apache-2.0
base_model: VTXAI/vtx-jev-mod-multilingual
pipeline_tag: feature-extraction
language:
- multilingual
- en
- hi
- bn
- ta
- te
- mr
- ur
- ar
- es
- ru
- zh
tags:
- sentence-transformers
- static-embeddings
- model2vec
- moderation
- safety
- abuse-detection
- lf2
- 2bit-quantization
- cpu-optimized
---

# 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).

---

### 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`.

---

### Quick Start: Content Moderation & Toxicity Scoring

Using native anchor vector matching:

```python
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}
```

---

### Usage with Sentence Transformers

```python
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)
```

---

### 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%** |