---
language:
  - en
  - ru
license: apache-2.0
library_name: transformers
pipeline_tag: translation
tags:
  - translation
  - LiteRT
  - safetensors
  - Helsinki-NLP/tatoeba
  - openlanguagedata/flores_plus
  - marian
  - onnx
  - teradata
base_model: Helsinki-NLP/opus-mt_tiny_eng-rus
---

> ⚠️ **See [Disclaimer](#disclaimer) below before using.**

# opus-mt_tiny_eng-rus  🇬🇧 → 🇷🇺

## A Teradata-compatible Translation Model

A sequence-to-sequence translation model that translates text from
**English 🇬🇧** to
**Russian 🇷🇺**.
This repository hosts an ONNX-converted version of
[Helsinki-NLP/opus-mt_tiny_eng-rus](https://huggingface.co/Helsinki-NLP/opus-mt_tiny_eng-rus),
packaged for use with the Teradata `mldb.ONNXSeq2Seq` BYOM function.

**This repository does not redistribute the original model weights.** It contains only:

- `onnx/model-fp32.onnx` — full-precision ONNX graph
- `onnx/model-int8.onnx` — dynamically quantized ONNX graph
- `tokenizer.json` — repacked Marian tokenizer suitable for BYOM
- `config.json` — model architecture metadata, copied unchanged from the upstream repo
- `generation_config.json` — generation defaults, copied unchanged from the upstream repo

For the original PyTorch weights and training details, see the upstream model:
**[Helsinki-NLP/opus-mt_tiny_eng-rus](https://huggingface.co/Helsinki-NLP/opus-mt_tiny_eng-rus)**.

**Specifications**

| | |
|---|---|
| Source language | English 🇬🇧 (`en`) |
| Target language | Russian 🇷🇺 (`ru`) |
| Architecture | MarianMT (encoder-decoder) |
| Max input tokens | 256 |
| Max output tokens | 512 |
| ONNX file sizes | fp32 (177 MB), int8 (94 MB) |
| ONNX opset | 14 |
| ONNX IR version | 8 (BYOM 7.0+ compatible) |
| License | Apache-2.0 (from upstream) |
| Reference | https://huggingface.co/Helsinki-NLP/opus-mt_tiny_eng-rus |

Generation parameters are configurable at SQL time via the
`mldb.ONNXSeq2Seq` USING clause through `Const_*` keys: `Const_min_length`,
`Const_max_length`, `Const_num_beams`, `Const_length_penalty`,
`Const_repetition_penalty`. They are not fixed in the ONNX graph.
(`num_return_sequences` is the exception — it's baked into the graph as 1.)

## Quickstart: Deploying this Model in Teradata

Requires Teradata 17.20+ with **BYOM 7.0.0.4** or newer (the conversion
targets ONNX IR version 8, which BYOM 7.0.x requires).

```python
import getpass
import teradataml as tdml
from huggingface_hub import hf_hub_download

repo_id  = "Teradata/opus-mt_tiny_eng-rus"
model_id = "opus-mt_tiny_eng-rus"          # used as BYOM model_id

# 1. Download artifacts from this repo
hf_hub_download(repo_id=repo_id, filename="onnx/model-int8.onnx", local_dir="./")
hf_hub_download(repo_id=repo_id, filename="tokenizer.json",       local_dir="./")

# 2. Connect to Teradata
tdml.create_context(
    host=input("host: "),
    username=input("user: "),
    password=getpass.getpass("password: "),
)

# 3. Load model + tokenizer into BYOM tables
tdml.save_byom(model_id=model_id, model_file="onnx/model-int8.onnx",
               table_name="translation_models")
tdml.save_byom(model_id=model_id, model_file="tokenizer.json",
               table_name="translation_tokenizers")

# 4. Translate
query = f"""
SELECT id, sequences
FROM mldb.ONNXSeq2Seq(
    ON (SELECT id, txt FROM your_input_table) AS InputTable
    ON (SELECT model_id, model FROM translation_models
         WHERE model_id = '{{model_id}}') AS ModelTable DIMENSION
    ON (SELECT model AS tokenizer FROM translation_tokenizers
         WHERE model_id = '{{model_id}}') AS TokenizerTable DIMENSION
    USING
        Accumulate('id')
        ModelOutputTensor('sequences')
        SkipSpecialTokens('true')
        OutputLength(512)
        OverwriteCachedModel('{{model_id}}')
        Const_min_length(1)
        Const_max_length(64)
        Const_num_beams(4)
        Const_length_penalty(1.0)
        Const_repetition_penalty(1.0)
) AS t
"""
print(tdml.DataFrame.from_query(query))
```

Use `model-int8.onnx` unless you have a measured accuracy reason to ship `fp32`.

## How this model was converted

This model was produced with the open-source
[`teradata-opus-translate`](https://pypi.org/project/teradata-opus-translate/)
package, which exports the encoder/decoder, stitches in the BeamSearch op,
applies dynamic int8 quantization, and verifies parity against PyTorch on a
small sample set.

> **Note:** the same package can convert *any* Helsinki-NLP MarianMT model
> (including ones not in this collection) to a BYOM-ready ONNX bundle. If
> you have a translation pair that's not published here, install the package
> and run:
>
> ```python
> from teradata_opus_translate import convert_model, convert_tokenizer
>
> convert_model(
>     "Helsinki-NLP/<your-model>",
>     output_path="model-int8.onnx",
>     precision="int8",
> )
> convert_tokenizer(
>     "Helsinki-NLP/<your-model>",
>     output_path="tokenizer.json",
> )
> ```
>
> The resulting `model-int8.onnx` and `tokenizer.json` are ready to deploy
> with the Quickstart flow above.

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