🌐 ViuTranslate-500M

High-Performance English ↔ Hindi Neural Machine Translation Foundation Model

🌐 ViuAI Studio | 📚 Translation Dataset | 📐 Architecture | ⚡ Quickstart | 🚀 5090 Runner

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📌 Introduction

ViuTranslate-500M is a production-grade bilingual Neural Machine Translation (NMT) foundation model developed by ViuAI. Built on the custom Sarus-500M decoder-only transformer architecture, it is engineered specifically for fluid, high-fidelity bidirectional translation between English and Hindi (Devanagari).

Unlike general-purpose conversational LLMs that frequently inject conversational fluff, unsolicited commentary, or mathematical reasoning hallucinations, ViuTranslate-500M is optimized purely for deterministic, end-to-end translation with native-speaker fluency.

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🌟 Key Capabilities & Highlights

1. ⚡ Google-Translate Style Direct Input:
   • Accepts raw, untagged sentences directly. Input plain English to receive pure Hindi; input plain Hindi to receive pure English.
2. 💬 Command / Instruction Mode Support:
   • Seamlessly handles explicit prompts (e.g., Translate to Hindi: ... and Translate to English: ...).
3. 🛡️ Zero Synthetic Data Guarantee:
   • Trained exclusively on authentic, human-verified parallel corpora from top research institutions (CFILT IIT Bombay and AI4Bharat Samanantar). Zero synthetic, template-generated, or LLM-distilled text.
4. 🔤 100% Devanagari Unicode Coverage:
   • Custom 64,003-vocab byte-fallback BPE tokenizer ensures exactly 0 <unk> tokens across standard benchmark corpora.
5. 🚀 Ultra-Low Latency & Edge-Ready:
   • Sub-15ms generation per sentence on modern GPUs (RTX 5090, RTX 4090, A100, T4) with efficient Grouped Query Attention (GQA).

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📐 Model Specifications

Parameter	Specification	Details
Model Name	ViuTranslate-500M	ViuAI Translation Engine
Base Architecture	Sarus-500M	Decoder-only Autoregressive Transformer
Total Parameters	500,642,560 (~500M)	Optimal size for high translation density & fast inference
Hidden Dimension ($d_{\text{model}}$)	1280	Latent semantic space
Transformer Layers	24	Balanced depth for deep bilingual representations
Attention Heads	20 Query / 4 KV Heads	Grouped Query Attention (GQA 5:1 ratio)
Intermediate Size (FFN)	3456	SwiGLU non-linear projection
Vocabulary Size	64,003	Byte-fallback BPE with full Devanagari coverage
Context Length	2048 Tokens	Rotary Positional Embeddings (RoPE, $\theta = 10,000$)
Normalization	RMSNorm	Root Mean Square Layer Normalization ($\epsilon = 10^{-6}$)
Precision	BFloat16 / FP16	Native Blackwell, Ada Lovelace, Ampere support

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📚 Training Corpus & Provenance

The model is trained on the curated dataset hosted at ViuAI/ViuTranslate-Data:

Dataset Source	Contributing Institution	Curated Pairs	Description
IIT Bombay English-Hindi Corpus	CFILT, IIT Bombay	50,000	Gold-standard academic corpus covering news, judicial, and literature
AI4Bharat Samanantar	IIT Madras / AI4Bharat	50,000	Large-scale verified Indian language web & publication texts
IIT Bombay Benchmark Test Set	CFILT, IIT Bombay	2,502	Standardized international evaluation test suite
Total Curated Dataset	—	102,502 Pairs	13.6 Million Active Training Tokens

🛡️ Mathematical Quality Guardrails:

• Length Ratio Bound: Enforced $0.40 \le \frac{\text{len(en)}}{\text{len(hi)}} \le 2.40$ to eliminate truncated pairs.
• Script Purity: Minimum 50% Latin characters on English side; minimum 40% Devanagari characters ($[\u0900-\u097F]$) on Hindi side.
• Hygiene Sanitization: 100% stripped of HTML tags, XML entities, programming code blocks, and URLs.
• Bidirectional Augmentation: Trained in both Direct Mode and Command Mode in both directions ($EN \leftrightarrow HI$).

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🚀 Quickstart & Inference

1. Installation

pip install torch tokenizers huggingface_hub

2. Standalone Python Inference

import torch
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download

# Download model code and tokenizer
repo_id = "ViuAI/ViuTranslate"
tok_path = hf_hub_download(repo_id=repo_id, filename="tokenizer.json")
config_path = hf_hub_download(repo_id=repo_id, filename="config.py")
model_path = hf_hub_download(repo_id=repo_id, filename="model.py")

# Import architecture
import sys, os
sys.path.insert(0, os.path.dirname(config_path))
from config import ViuAIConfig
from model import ViuAI

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = Tokenizer.from_file(tok_path)
cfg = ViuAIConfig(vocab_size=64003, context_length=2048)
model = ViuAI(cfg).to(device)

# Load weights (supports base checkpoint or fine-tuned weights)
try:
    ckpt_path = hf_hub_download(repo_id=repo_id, filename="viutranslate_final.pt")
except Exception:
    ckpt_path = hf_hub_download(repo_id="ViuAI/ViuAI-500M", filename="checkpoints/ckpt_latest.pt")

state = torch.load(ckpt_path, map_location=device, weights_only=False)
model.load_state_dict(state.get("model_state_dict", state), strict=False)
model.eval()

def translate(text: str) -> str:
    prompt = f"<|user|>\n{text.strip()}<|endofturn|>\n<|assistant|>\n"
    inp = torch.tensor([tokenizer.encode(prompt).ids], device=device)
    with torch.no_grad():
        out = model.generate(inp, max_new_tokens=128, temperature=0.2, top_p=0.9, eos_token_id=64002)
    return tokenizer.decode(out[0][inp.shape[1]:].tolist()).replace("<|endofturn|>", "").strip()

# Examples
print("EN -> HI:", translate("Consistency and discipline are the keys to long term success."))
print("HI -> EN:", translate("सूरज पूर्व में उगता है और पश्चिम में डूबता है।"))

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🏋️ Training on RTX 5090 (32GB)

Run the ultra-optimized 1-click training launcher directly from your terminal:

# Download and launch runner
curl -O https://huggingface.co/ViuAI/ViuTranslate/raw/main/runners/run_5090.py
python run_5090.py

• Target Hardware: NVIDIA GeForce RTX 5090 (32GB VRAM, Blackwell Architecture)
• Configuration: micro_batch=32, grad_accum=2 (Effective Batch = 64), bfloat16
• Speed: 100,000 tokens/sec (6.5 minutes for full 100K training run)

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📁 Repository Structure

ViuAI/ViuTranslate
├── README.md                          # Official Model Card & Documentation
├── config.py                          # Sarus-500M Architecture Config
├── model.py                           # PyTorch Transformer Definition
├── tokenizer.json                     # 64,003 Byte-Fallback Tokenizer
├── tokenizer_config.json              # Fast Tokenizer Configuration
├── special_tokens_map.json            # Turn & Separation Tokens
├── generation_config.json             # Recommended Decoding Parameters
│
├── scripts/                           # Modular Pipeline Scripts
│   ├── train.py                       # High-Throughput SFT Training Engine
│   ├── inference.py                   # Interactive CLI Translation Console
│   └── evaluate.py                    # BLEU / chrF++ Benchmark Evaluator
│
└── runners/                           # 1-Click Hardware Launchers
    ├── run_5090.py                    # RTX 5090 Ultra-Fast Launcher
    └── run_kaggle.py                  # Kaggle GPU T4 Launcher

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📜 Citation & Reference

@misc{viutranslate2026,
  author = {ViuAI Research Team},
  title = {ViuTranslate-500M: High-Performance English-Hindi Neural Machine Translation Foundation Model},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/ViuAI/ViuTranslate}}
}