Mungert / Seed-X-PPO-7B-GGUF

Seed-X-PPO-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 793c0d7f .

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Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

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Click here to get info on choosing the right GGUF model format

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Seed-X-PPO-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-PPO model, with the following features:

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

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

Model Downloads

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

from vllm import LLM, SamplingParams, BeamSearchParams

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

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:

@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}, 
}

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks :

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor . You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test :
Choose an AI assistant type :

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring , specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs ) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI , let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test :

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source . Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊