---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-az
base_model_relation: quantized
tags:
  - onnx
  - translation
  - marian
library_name: transformers
pipeline_tag: translation
language:
  - en
  - az
---

# opus-mt-en-az-onnx

ONNX export (fp32 + dynamic int8) of [`Helsinki-NLP/opus-mt-en-az`](https://huggingface.co/Helsinki-NLP/opus-mt-en-az),
a Marian (en -> az) translation model from the Helsinki-NLP OPUS-MT project.

**License:** apache-2.0, inherited unchanged from the base model.

## Contents

| Files | What it is |
|---|---|
| `encoder_model.onnx`, `decoder_model.onnx`, `decoder_with_past_model.onnx` | ONNX, float32 |
| `int8/` | same three graphs, dynamic int8 (`QInt8`, `MatMul` only, `/lm_head/MatMul` excluded) |
| `source.spm`, `target.spm`, `vocab.json` | `MarianTokenizer` over the original sentencepiece models |

fp32 size: 485 MB (three graphs) | int8 size: 295 MB

## Usage

```python
from optimum.onnxruntime import ORTModelForSeq2SeqLM
from transformers import AutoTokenizer

repo = "TigreGotico/opus-mt-en-az-onnx"
tok = AutoTokenizer.from_pretrained(repo)
model = ORTModelForSeq2SeqLM.from_pretrained(repo, use_cache=True, use_merged=False)  # fp32
# int8: ORTModelForSeq2SeqLM.from_pretrained(repo, subfolder="int8", use_cache=True, use_merged=False)
inputs = tok("The weather is very nice today.", return_tensors="pt")
out = model.generate(**inputs, num_beams=4, max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))
```

## Parity with the original PyTorch model

10 general-domain sentences, exact-string-match of generated output against
`MarianMTModel.generate()` on the original `Helsinki-NLP/opus-mt-en-az` checkpoint.

```yaml
parity:
  fp32_greedy: 1.00   # 10/10
  fp32_beam4:  1.00   # 10/10
  int8_greedy: 0.90   # 9/10
  int8_beam4:  0.40   # 4/10
```

| Decoding | fp32 exact match | int8 exact match |
|---|---|---|
| greedy (num_beams=1) | 10/10 (100.0%) | 9/10 (90.0%) |
| beam=4 | 10/10 (100.0%) | 4/10 (40.0%) |

fp32 is a faithful reproduction of the original model at both decoding
settings. int8 dynamic quantization noticeably degrades quality on this
checkpoint under beam search - a spot check found real semantic drift on
longer sentences (not just paraphrase), e.g. for "We need to discuss the
budget for next quarter." the int8 beam-4 output diverged from both the
reference and the fp32 ONNX output. Prefer fp32 for this pair; int8 is
provided for size-constrained deployments where greedy decoding is used and
some quality loss is acceptable.