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thanhh12/
Seed-X-PPO-7B-GPTQ-INT8

Name: Seed-X-PPO-7B-GPTQ-INT8
Availability: InStock

safetensors

mistral

translation

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Seed-X-PPO-7B-GPTQ-Int8

Introduction

We are excited to introduce Seed-X, a powerful series of open-source multilingual translation language models, including an instruction model, a reinforcement learning model, and a reward model. It pushes the boundaries of translation capabilities within 7 billion parameters. We develop Seed-X as an accessible, off-the-shelf tool to support the community in advancing translation research and applications:

Exceptional translation capabilities: Seed-X exhibits state-of-the-art translation capabilities, on par with or outperforming ultra-large models like Gemini-2.5, Claude-3.5, and GPT-4, as validated by human evaluations and automatic metrics.
Deployment and inference-friendly: With a compact 7B parameter count and mistral architecture, Seed-X offers outstanding translation performance in a lightweight and efficient package, ideal for deployment and inference.
Broad domain coverage: Seed-X excels on a highly challenging translation test set spanning diverse domains, including the internet, science and technology, office dialogues, e-commerce, biomedicine, finance, law, literature, and entertainment.
performance

This repo contains the Seed-X-PPO-7B-GPTQ-Int8 model, with the following features:

Type: Causal language models
Training Stage: Pretraining & Post-training
Support: Multilingual translation among 28 languages
Quantization: GPTQ 8-bit

（We recommend using Seed-X-PPO model, as its translation performance is superior to Seed-X-Instruct.）

Languages	Abbr.	Languages	Abbr.	Languages	Abbr.	Languages	Abbr.
Arabic	ar	French	fr	Malay	ms	Russian	ru
Czech	cs	Croatian	hr	Norwegian Bokmal	nb	Swedish	sv
Danish	da	Hungarian	hu	Dutch	nl	Thai	th
German	de	Indonesian	id	Norwegian	no	Turkish	tr
English	en	Italian	it	Polish	pl	Ukrainian	uk
Spanish	es	Japanese	ja	Portuguese	pt	Vietnamese	vi
Finnish	fi	Korean	ko	Romanian	ro	Chinese	zh

Model Downloads

Model Name	Description	Download
Seed-X-Instruct	Instruction-tuned for alignment with user intent.	🤗 Model
Seed-X-PPO	RL trained to boost translation capabilities.	🤗 Model
👉 Seed-X-PPO-GPTQ-Int8	Quantization: GPTQ 8-bit.	🤗 Model
Seed-X-PPO-AWQ-Int4	Quantization: AWQ 4-bit.	🤗 Model
Seed-X-RM	Reward model to evaluate the quality of translation.	🤗 Model

Quickstart

📮 Notice

The language tags at the end of the prompt is necessary, which are used in PPO training. For example, when the target language is German, <de> needs to be added. You can refer to the above table for language abbreviations.
This model is specialized in multilingual translation, which is unexpected to support other tasks.
We don't have any chat template, thus you don't have to perform tokenizer.apply_chat_template. Please avoid prompting the model in a multi-round conversation format.
We recommend against using unofficial quantized versions for local deployment. We will soon release an official quantized model and develop a demo on Hugging Face Space.

Here is a simple example demonstrating how to load the model and perform translation using vllm

Recommended:vllm==0.8.0, transformers==4.51.3

python

from vllm import LLM, SamplingParams, BeamSearchParams
model_path = "./ByteDance-Seed/Seed-X-PPO-7B-GPTQ-Int8"
model = LLM(model=model_path,
            max_num_seqs=512,
            tensor_parallel_size=8,
            enable_prefix_caching=True, 
            gpu_memory_utilization=0.95)
messages = [
    # without CoT
    "Translate the following English sentence into Chinese:\nMay the force be with you <zh>",
    # with CoT
    "Translate the following English sentence into Chinese and explain it in detail:\nMay the force be with you <zh>" 
]
# Beam search (We recommend using beam search decoding)
decoding_params = BeamSearchParams(beam_width=4,
                                   max_tokens=512)
# Greedy decoding
decoding_params = SamplingParams(temperature=0,
                                 max_tokens=512,
                                 skip_special_tokens=True)
results = model.generate(messages, decoding_params)
responses = [res.outputs[0].text.strip() for res in results]
print(responses)

Evaluation

We evaluated Seed-X on a diverse set of translation benchmarks, including FLORES-200, WMT-25, and a publicly released challenge set accompanied by human evaluations.

For detailed benchmark results and analysis, please refer to our Technical Report.

License

This project is licensed under OpenMDW. See the LICENSE file for details.

Citation

If you find Seed-X useful for your research and applications, feel free to give us a star ⭐ or cite us using:

bibtex

@misc{cheng2025seedxbuildingstrongmultilingual,
      title={Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters}, 
      author={Shanbo Cheng and Yu Bao and Qian Cao and Luyang Huang and Liyan Kang and Zhicheng Liu and Yu Lu and Wenhao Zhu and Jingwen Chen and Zhichao Huang and Tao Li and Yifu Li and Huiying Lin and Sitong Liu and Ningxin Peng and Shuaijie She and Lu Xu and Nuo Xu and Sen Yang and Runsheng Yu and Yiming Yu and Liehao Zou and Hang Li and Lu Lu and Yuxuan Wang and Yonghui Wu},
      year={2025},
      eprint={2507.13618},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.13618}, 
}

thanhh12/Seed-X-PPO-7B-GPTQ-INT8