Design upgrade: Sleek tier-1 model card matching ViuAI-500M standards

78688bc verified 5 days ago

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	---
	license: apache-2.0
	language:
	- en
	- hi
	tags:
	- viuai
	- viutranslate
	- sarus-500m
	- translation
	- nmt
	- english-to-hindi
	- hindi-to-english
	- indic
	- devanagari
	- bfloat16
	- pytorch
	- zero-synthetic
	datasets:
	- cfilt/iitb-english-hindi
	- ai4bharat/samanantar
	pipeline_tag: translation
	library_name: transformers
	inference: false
	---

	<div align="center">

	# 🌐 ViuTranslate-500M

	### High-Performance English ↔ Hindi Neural Machine Translation Foundation Model

	[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Parameters](https://img.shields.io/badge/Parameters-500M-green.svg)](#-model-specifications)
	[![Context](https://img.shields.io/badge/Context%20Length-2048-orange.svg)](#-model-specifications)
	[![Dataset](https://img.shields.io/badge/Corpus-100K%2B%20Real%20Academic-purple.svg)](https://huggingface.co/datasets/ViuAI/ViuTranslate-Data)
	[![Languages](https://img.shields.io/badge/Languages-English%20%7C%20Hindi-red.svg)](#-translation-capabilities)
	[![Data Quality](https://img.shields.io/badge/Data-100%25%20Human%20Verified-brightgreen.svg)](#-zero-synthetic-data-guarantee)

	[🌐 ViuAI Studio](https://github.com/ViuAI) \| [📚 Translation Dataset](https://huggingface.co/datasets/ViuAI/ViuTranslate-Data) \| [📐 Architecture](#-model-specifications) \| [⚡ Quickstart](#-quickstart--inference) \| [🚀 5090 Runner](#-training-on-rtx-5090)

	---

	</div>

	## 📌 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.

	---

	## 🌟 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).

	---

	## 📐 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 \|

	---

	## 📚 Training Corpus & Provenance

	The model is trained on the curated dataset hosted at [`ViuAI/ViuTranslate-Data`](https://huggingface.co/datasets/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 ($EN \leftrightarrow HI$).

	---

	## 🚀 Quickstart & Inference

	### 1. Installation

	```bash
	pip install torch tokenizers huggingface_hub
	```

	### 2. Standalone Python Inference

	```python
	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।"))
	```

	---

	## 🏋️ Training on RTX 5090 (32GB)

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

	```bash
	# 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)

	---

	## 📁 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
	```

	---

	## 📜 Citation & Reference

	```bibtex
	@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}}
	}
	```