ByteDance-Seed / Seed-X-Instruct-7B

Seed-X-Instruct-7B

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.

This repo contains the Seed-X-Instruct model, with the following features:

• Type: Causal language models
• Training Stage: Pretraining & Post-training
• Support: Multilingual translation among 28 languages

Model Downloads

Quickstart

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

from vllm import LLM, SamplingParams

model_path = "./ByteDance-Seed/Seed-X-Instruct-7B"

model = LLM(model=model_path,
            max_num_seqs=512,
            tensor_parallel_size=8,
            enable_prefix_caching=True, 
            gpu_memory_utilization=0.95)

messages = [
    "Translate the following English sentence into Chinese:\nMay the force be with you <zh>", # without CoT
    "Translate the following English sentence into Chinese and explain it in detail:\nMay the force be with you <zh>" # with CoT
]

# Sampling
decoding_params = SamplingParams(temperature=0,
                                 max_tokens=512,
                                 skip_special_tokens=True)
# Beam Search
decoding_params = BeamSearchParams(beam_width=4, 
                                    max_tokens=512)

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

We will soon publish our technical report on Arxiv.