Qwen2.5-7B-d2d
This model is released as part of our paper Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking. The code and paper-specific inference scripts are available in the Doc2FRC GitHub repository.
Qwen2.5-7B-d2d is a full-parameter fine-tuned version of Qwen/Qwen2.5-7B-Instruct for multilingual document-level machine translation. It was fine-tuned on sardinelab/DocBlocks.
This model is trained directly on the document-to-document (d2d) translation task.
Supported translation directions
The model supports translation between English and the following languages in both directions:
- German
- Spanish
- French
- Italian
- Korean
- Dutch
- Portuguese
- Russian
- Chinese
General usage
The example below demonstrates general model usage. For the exact inference scripts, prompting setup, and evaluation procedure used in the paper, please refer to the Doc2FRC GitHub repository.
Recommended prompt format
The model was fine-tuned with the following raw ChatML-style translation prompt:
<|im_start|>user
Translate the following source text from {SOURCE_LANGUAGE} into {TARGET_LANGUAGE}.
{SOURCE_LANGUAGE}: {SOURCE_TEXT}.
{TARGET_LANGUAGE}: <|im_end|>
<|im_start|>assistant
Use full English language names such as English, Chinese, German, or Russian.
Transformers example
Install a PyTorch build appropriate for your hardware, together with Transformers and Accelerate. PyTorch 2.6 or later is recommended for loading the current PyTorch .bin checkpoint files.
pip install "transformers>=4.56.2" accelerate
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "ynklab/Qwen2.5-7B-d2d"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
source_language = "English"
target_language = "Chinese"
source_text = "The weather is nice today"
prompt = (
"<|im_start|>user\n"
f"Translate the following source text from {source_language} "
f"into {target_language}.\n"
f"{source_language}: {source_text}.\n"
f"{target_language}: <|im_end|>\n"
"<|im_start|>assistant\n"
)
inputs = tokenizer(
prompt,
return_tensors="pt",
add_special_tokens=False,
)
inputs = {name: tensor.to(model.device) for name, tensor in inputs.items()}
with torch.inference_mode():
outputs = model.generate(
**inputs,
max_new_tokens=16384,
do_sample=False,
repetition_penalty=1.05,
)
generated_tokens = outputs[0, inputs["input_ids"].shape[1]:]
translation = tokenizer.decode(
generated_tokens,
skip_special_tokens=True,
).strip()
print(translation)
For best results, keep the combined prompt and generated translation within 32,768 tokens.
Training
- Base model: Qwen2.5-7B-Instruct
- Training method: full-parameter supervised fine-tuning
- Training data: DocBlocks document-level parallel data
- Epochs: 2
- Learning rate: 7e-6
- Learning-rate scheduler: cosine
- Warmup steps: 125
- Maximum sequence length: 32,768 tokens
- Training precision: bfloat16
- Optimizer: AdamW
- Weight decay: 0.01
License
This model is released under the Apache License 2.0. See the LICENSE file for details.
DocBlocks contains material derived from multiple sources. Users should also consult the DocBlocks dataset and the original data sources for their applicable licensing conditions.
Acknowledgements
This model is based on Qwen2.5-7B-Instruct and was fine-tuned using DocBlocks. Please cite our paper when using this model in academic work.
Citation
@misc{wang2026doc2frclengthconsistentdocumentlevelmachine,
title={Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking},
author={Xiaotian Wang and Youyuan Lin and Zhan Shen and Hitomi Yanaka},
year={2026},
eprint={2609.12674},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2609.12674},
}