VaishalBusiness/opus

🌍 VGT ONNX Model Hub

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Welcome to the VGT (Vaishal's Global Translator) ONNX Model Hub — a large-scale open-source collection of ~1,000+ pretrained models converted to ONNX for blazing fast inference, research, and production deployment.

This hub brings together models from leading open-source projects (like Helsinki-NLP MarianMT) and makes them universally accessible via ONNX.

✨ Key highlights:

• ✅ 1,000+ ONNX models for translation, NLP, and beyond
• ✅ Plug-and-play with Hugging Face tokenizers
• ✅ Optimized for fast inference with ONNX Runtime
• ✅ Easy to fetch programmatically (no manual downloads!)
• ✅ Fully open-source, respecting original licenses

🔗 Browse the complete catalog here: yet to add

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📂 Repository Structure

Each model lives in its own folder, for example:

Helsinki-NLP-opus-mt-tc-base-bat-zle/
├── config.json
├── decoder_model.onnx
├── decoder_model_merged.onnx
├── decoder_with_past_model.onnx
├── encoder_model.onnx
├── generation_config.json
├── source.spm
├── target.spm
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.json

File breakdown:

• encoder_model.onnx → encoder graph
• decoder_model.onnx → base decoder
• decoder_model_merged.onnx → optimized decoder (recommended for speed)
• decoder_with_past_model.onnx → decoder with caching (past key/values)
• Tokenizer files → vocab.json, source.spm, target.spm, etc.
• Configs → model + tokenizer configs

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🚀 Usage

1. Install dependencies

pip install huggingface_hub onnxruntime transformers sentencepiece

2. Fetch and use a model programmatically

Example with Helsinki-NLP-opus-mt-tc-base-bat-zle (replace with any model name from the catalog):

from huggingface_hub import snapshot_download
import onnxruntime as ort
from transformers import MarianTokenizer
import numpy as np

# 🔹 Step 1: Download the model folder (cached in ~/.cache/huggingface)
model_dir = snapshot_download(
    repo_id="VaishalBusiness/opus",
    allow_patterns="Helsinki-NLP-opus-mt-tc-base-bat-zle/*"
)

# 🔹 Step 2: Load tokenizer
tokenizer = MarianTokenizer.from_pretrained(f"{model_dir}/Helsinki-NLP-opus-mt-tc-base-bat-zle")

# 🔹 Step 3: Encode input
inputs = tokenizer("Hello, how are you?", return_tensors="np")

# 🔹 Step 4: Run encoder
enc = ort.InferenceSession(f"{model_dir}/Helsinki-NLP-opus-mt-tc-base-bat-zle/encoder_model.onnx")
enc_out = enc.run(None, {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"]
})

# 🔹 Step 5: Run merged decoder
dec = ort.InferenceSession(f"{model_dir}/Helsinki-NLP-opus-mt-tc-base-bat-zle/decoder_model_merged.onnx")

decoder_input_ids = np.array([[tokenizer.pad_token_id]], dtype=np.int64)
out = dec.run(None, {
    "input_ids": decoder_input_ids,
    "encoder_hidden_states": enc_out[0]
})

print(out)  # model outputs

✅ This code works for any model in the hub. Just replace Helsinki-NLP-opus-mt-tc-base-bat-zle with your desired model folder name.

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📋 Model Catalog

• The full catalog of ~the models is available at yet to add.

• Each entry includes:

  • 📌 Model identifier (e.g. Helsinki-NLP-opus-mt-tc-base-bat-zle)
  • 🔗 Direct download links to ONNX artifacts
  • 🗂 Tokenizer + config files
  • 📖 Original Hugging Face model card

We recommend filtering by language pairs or model family when browsing.

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📝 Attribution

This project would not exist without the incredible open-source community.

• All models come from Helsinki-NLP, a cornerstone of multilingual machine translation.
• The Hugging Face ecosystem provides model hosting, tokenizers, and configs.
• The ONNX community enables fast inference across platforms.

🙏 Huge thanks to all original authors and contributors.

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📜 Licensing

• Each model retains the license of its original version.
• This hub provides ONNX conversions only; licenses are not overridden.
• Users are responsible for complying with the license terms of each model.
• When using a model, please cite the original authors and respect attribution requirements.

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🤝 Contributing

We welcome contributions!

• Found a model that needs fixing? Submit a PR.
• Want to add a new ONNX conversion? We’d love to include it.
• Issues and improvements are always appreciated.

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⚡ Final Notes

This hub is designed to make state-of-the-art NLP models available at scale, ready to drop into production or research pipelines. With 1,440+ ONNX models, you can cover a vast range of languages and tasks, all with the speed of ONNX Runtime.

🔗 Explore → yet to add

🚀 Deploy → Hugging Face Hub + ONNX Runtime

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