VibeThinker-1.5B-GGUF

<span style="color: #7FFF7F;">VibeThinker-1.5B GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit 1d45b4228.

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VibeThinker-1.5B

<p align="center">📁 <a href="https://github.com/WeiboAI/VibeThinker">Github</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/models/WeiboAI/VibeThinker-1.5B">Model Scope</a> | &nbsp&nbsp📄 <a href="https://arxiv.org/abs/2511.06221">Techical Report</a></p>

Introduction

VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium.

Key Performance Data

💡 Mathematical Reasoning: On the three major math benchmarks AIME24, AIME25, and HMMT25, its scores (80.3, 74.4, and 50.4, respectively) all surpass those of the initial DeepSeek R1 model, which has over 400 times the parameters (scores of 79.8, 70.0, and 41.7, respectively).

🌱 Code Generation: It achieved scores of 55.9 on LiveCodeBench v5 and 51.1 on v6. Its v6 score slightly leads Magistral Medium (50.3), underscoring its strong reasoning performance.

🔁 On the AIME 25 benchmark, VibeThinker-1.5B significantly extends the Pareto frontier of reasoning accuracy versus model scale, demonstrating that exceptional performance can be achieved with extreme parameter efficiency.

Training Pipeline

VibeThinker-1.5B's core innovation lies in the "Spectrum-to-Signal Principle" (SSP) training framework: it first explores solution diversity during the Supervised Fine-Tuning (SFT) stage, and then optimizes its policy to reinforce correct signals in the Reinforcement Learning (RL) stage. By systematically integrating these two phases, our approach establishes diversity as the central technical design principle, enabling VibeThinker-1.5B to achieve robust performance that surpasses conventional training paradigms.

Usage Guidelines

We recommend using this model for competitive-style math and coding problems.

To facilitate quick verification by the community, we recommend the following parameter settings: temperature: 0.6 or 1.0, max token length: 40960, top_p: 0.95, top_k: -1.

A more detailed evaluation scheme we have prepared can be found on GitHub.

Quick Start

Required: transformers>=4.54.0

Recommended for better inference performance: vLLM==0.10.1 or SGLang>=0.4.9.post6

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig


class VibeThinker:
    def __init__(self, model_path):
        self.model_path = model_path
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_path,
            low_cpu_mem_usage=True,
            torch_dtype="bfloat16",
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)

    def infer_text(self, prompt):
        messages = [
            {"role": "user", "content": prompt}
        ]
        text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)

        text = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)

        generation_config = dict(
            max_new_tokens=40960,
            do_sample=True,
            temperature=0.6, # 0.6 or 1.0, you can set it according to your needs
            top_p=0.95,
            top_k=None # in vLLM or SGlang, please set top_k to -1, it means skip top_k for sampling
        )
        generated_ids = self.model.generate(
            **model_inputs,
            generation_config=GenerationConfig(**generation_config)
        )
        generated_ids = [
            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
        ]

        response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

        return response


if __name__ == '__main__':
    model = VibeThinker('Your model path')
    prompt = 'Your Prompt'
    print(model.infer_text(prompt))

License

The model repository is licensed under the MIT License.

Citations & References

If you use VibeThinker in your research or product, please cite:

@misc{xu2025tinymodelbiglogic,
      title={Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B}, 
      author={Sen Xu and Yi Zhou and Wei Wang and Jixin Min and Zhibin Yin and Yingwei Dai and Shixi Liu and Lianyu Pang and Yirong Chen and Junlin Zhang},
      year={2025},
      eprint={2511.06221},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2511.06221}, 
}

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<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

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
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🟡 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
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• 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.

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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.

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