ONNX export of projecte-aina/aina-translator-ca-zh, Projecte Aina's (Barcelona Supercomputing Center, Language Technologies Unit) Catalan → Chinese machine translation model, fine-tuned from facebook/m2m100_1.2B. All credit for training data, fine-tuning and evaluation goes to Projecte Aina — see the source model card for training details, BLEU/ChrF numbers, and paper reference.

Licence

apache-2.0, verbatim as declared on the source model card (projecte-aina/aina-translator-ca-zh). Same licence applies to this derived ONNX export.

Files

encoder_model.onnx(_data)                fp32 encoder
decoder_model.onnx(_data)                fp32 decoder (no cache)
decoder_with_past_model.onnx(_data)      fp32 decoder (with KV cache)
int8/encoder_model.onnx                  dynamic-quantized (uint8) encoder
int8/decoder_model.onnx                  dynamic-quantized (uint8) decoder
int8/decoder_with_past_model.onnx        dynamic-quantized (uint8) decoder w/ cache
sentencepiece.bpe.model, vocab.json, tokenizer_config.json, ...   tokenizer files (M2M100Tokenizer)

Export

Base architecture: M2M100ForConditionalGeneration (transformers model_type: m2m_100), fine-tuned by Projecte Aina from facebook/m2m100_1.2B.

bash

optimum-cli export onnx \
  --model projecte-aina/aina-translator-ca-zh \
  --task text2text-generation-with-past \
  --no-post-process \
  aina-translator-ca-zh-onnx

--no-post-process is required: optimum's decoder-merge step OOMs on this model size on constrained hardware. As a result the ONNX export ships an un-merged decoder_model.onnx (no cache) and decoder_with_past_model.onnx (with cache) instead of a single decoder_model_merged.onnx.

int8 dynamic quantization (optimum.onnxruntime.ORTQuantizer, AVX2 config) was applied to each of the three graphs.

Target-language mechanism

Same family as aina-translator-zh-ca-onnx but the reverse direction. Unlike the zh→ca checkpoint, this one's tokenizer_config.json does carry explicit src_lang: "ca" / tgt_lang: "zh" fields, and they are already the tokenizer's default — no extra argument is required at inference time. There is still no forced_bos_token_id in generation_config.json; a plain tokenizer(text) + model.generate() call is sufficient and yields Chinese output directly, exactly as in the upstream model card's usage example.

Parity

8 held-out Catalan sentences, num_beams=4, max_new_tokens=64, compared against the original PyTorch model (transformers.AutoModelForSeq2SeqLM) with identical decoding settings.

	exact-match rate
ONNX fp32	8/8 = 100.0%
ONNX int8 (dynamic)	not separately re-verified against reference; same graphs, expect near-parity

Sample translations observed (ca → zh):

Catalan	Chinese (Aina / ONNX, identical)
Benvingut al projecte Aina!	欢迎来到Aina项目！
Hola, com estàs avui?	你好，今天你好吗？
Avui fa bon temps.	今天天气很好。
M'agradaria reservar una taula per a dos.	我想为两个人预订一张桌子。
On és l'estació de tren?	火车站在哪里？
Aquest llibre és interessant.	这本书很有趣。
Els nens juguen al parc.	孩子们在公园里玩耍。
Necessito ajuda amb el meu ordinador.	我需要电脑方面的帮助。

Usage

python

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM

model_id = "TigreGotico/aina-translator-ca-zh-onnx"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = ORTModelForSeq2SeqLM.from_pretrained(model_id)  # fp32
# int8: ORTModelForSeq2SeqLM.from_pretrained(model_id, subfolder="int8")

text = "Benvingut al projecte Aina!"
ids = tokenizer(text, return_tensors="pt").input_ids
out = model.generate(ids, num_beams=4, max_new_tokens=64)
print(tokenizer.decode(out[0], skip_special_tokens=True))
# 欢迎来到Aina项目！

Attribution

All modeling and training work is by Projecte Aina (Language Technologies Unit, Barcelona Supercomputing Center) — this repository only republishes an ONNX conversion of their weights for offline/CPU inference. Source model: projecte-aina/aina-translator-ca-zh. Contact for the original model: langtech@bsc.es.