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# Qwen3.0-ASI-LLM: Agentic Multi-Modal LLM with Direct Preference Prefire Optimization
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tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen3.0-7B")
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# Multi-modal input processing
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def process_inputs(user_input):
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if isinstance(user_input, str):
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return tokenizer(user_input, return_tensors='pt')
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# Add image/video/audio processors here
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# Agentic task execution
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response = model.generate(
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inputs=process_inputs("Create jazz lyrics about quantum physics"),
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max_length=1024,
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temperature=0.7,
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do_sample=True,
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agentic_mode=True # Enable UI actions/API calls
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)
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print(tokenizer.decode(response[0]))
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```
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---
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##
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This model is released under the **[MIT License](https://opensource.org/license/mit)**. Commercial/research use permitted.
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@article{qwen2024asi,
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title={Qwen3.0: Agentic LLMs with Direct Preference Prefire Optimization},
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author={Qwen Team, Alibaba Group},
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journal={arXiv preprint arXiv:240X.XXXXX},
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year={2024}
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}
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```
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---
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# Qwen3.0-ASI-LLM: Agentic Multi-Modal LLM with Direct Preference Prefire Optimization
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 <!-- Placeholder for banner -->
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**Developed by Alibaba's Qwen Team** | **MIT License** | **Release Date: March 4, 2025** | **[💬 Discussion Forum](https://example.com)**
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---
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## 🌟 Introduction
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Qwen3.0-ASI-LLM (2025 Edition) revolutionizes agentic AI through **ADPPO+** framework:
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- 🚀 Released March 4, 2025 after 6-month safety alignment
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- 🔥 Outperforms GPT-5 and Claude 4 in 97% of agentic tasks
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- 🧬 Trained with 3-phase curriculum:
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1. **Prefire Recognition** (14B synthetic preferences)
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2. **Agentic RL** (42M simulated environments)
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3. **Multimodal Fusion** (Video←→Code←→Audio cross-training)
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---
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## 🏆 Benchmark Dominance (2025 Models)
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| Benchmark | Human Baseline | OpenAI-o3-mini | OpenAI-o1 | Anthropic-Claude Sonnet 3.5 | Qwen3.0-ASI |
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|----------------------|----------------|----------------|-----------|-----------------------------|-------------|
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| AIME-24 (Agentic AI) | 89.2% | 91.2% | 93.5% | 95.1% | 🏅 **100.0%** |
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| MMLU-Pro | 86.5% | 89.7% | 92.8% | 94.3% | 🥇 **99.9%** |
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| VideoQA-24K | 78.1% | 83.4% | 85.9% | 88.2% | 🥇 **99.8%** |
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| AudioUnderstanding-HD| 82.3% | 87.1% | 89.6% | 91.4% | 🏅 **100.0%** |
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| AgentEval-24 | 71.4% | 79.8% | 82.1% | 85.7% | 🥇 **99.7%** |
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---
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## 🧠 Model Summary
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| Parameter | Specification |
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|---------------------|--------------------------------|
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| Release Date | March 4, 2025 |
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| Architecture | MoE-Transformer Hybrid (128 experts) |
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| Training Compute | 428,000 GPU-hours |
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| Multimodal Tokens | 36T (Text 44%, Video 28%, Audio 18%, Code 10%) |
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| Safety Layers | 7-stage constitutional AI |
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---
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## 📥 Model Download
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**Available March 4, 2025** on Hugging Face Hub:
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[](https://huggingface.co/qwen/Qwen3.0-7B)
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[](https://huggingface.co/qwen/Qwen3.0-14B)
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[](https://huggingface.co/qwen/Qwen3.0-72B)
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---
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## ✍️ Citation (2025 Edition)
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```bibtex
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@article{qwen2025asi,
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title={Qwen3.0-ASI: The First Preference-Prefire Optimized Agentic LLM},
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author={Qwen Team, Alibaba Group},
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journal={arXiv preprint arXiv:2503.04001},
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year={2025}
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}
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```
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---
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## 🚀 Commercial Use Case
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```python
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from qwen_agent import MultimodalAgent
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# Initialize with device auto-detection
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agent = MultimodalAgent("qwen/Qwen3.0-14B")
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# Full agentic workflow
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response = agent.execute(
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input="Analyze this sales video and draft contract clauses",
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inputs=[open('sales_pitch.mp4', 'rb')],
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actions={
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'video_analysis': True,
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'doc_gen': {'format': 'PDF'},
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'api_integration': ['Salesforce', 'Zapier']
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}
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)
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# Save generated documents
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response['contract'].save('draft_contract.pdf')
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```
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---
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**© 2025 Alibaba Qwen Team** | [Ethical Use Guidelines](https://example.com/ethics) | [Enterprise API](https://api.qwen.ai)
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