VaishalBusiness/opus
By VaishalBusiness · ↓ 0 · ♥ 0 · Updated 2026-02-10
What's in this repo
This page lists the files in VaishalBusiness/opus, including decoder_model.onnx, decoder_model_merged.onnx, decoder_with_past_model.onnx and more.
- 2636 ONNX models (.onnx files)
- 8561 files, including 3949 .json, 2636 .onnx, 1316 .spm, 659 .md
- Last updated 2026-02-10
ONNX files
| File | Size | Download |
|---|---|---|
| Helsinki-NLP-opus-mt-NORTH_EU-NORTH_EU/decoder_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-NORTH_EU-NORTH_EU/decoder_model_merged.onnx | — | Download |
| Helsinki-NLP-opus-mt-NORTH_EU-NORTH_EU/decoder_with_past_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-NORTH_EU-NORTH_EU/encoder_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-ROMANCE-en/decoder_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-ROMANCE-en/decoder_model_merged.onnx | — | Download |
| Helsinki-NLP-opus-mt-ROMANCE-en/decoder_with_past_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-ROMANCE-en/encoder_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-SCANDINAVIA-SCANDINAVIA/decoder_model.onnx | — | Download |
| Helsinki-NLP-opus-mt-SCANDINAVIA-SCANDINAVIA/decoder_model_merged.onnx | — | Download |
Showing 10 of 2636 .onnx files — see the full file list on the right.
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
🌍 VGT ONNX Model Hub
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
📂 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
🚀 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.
📋 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
- 📌 Model identifier (e.g.
We recommend filtering by language pairs or model family when browsing.
📝 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.
📜 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.
🤝 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.
⚡ 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
Model info
Created: 2025-09-29
Updated: 2026-02-10
View on Hugging FaceFiles (8561)
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