mungert/mirothinker-v1.0-30b-gguf overview

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    "readme_markdown": "---\nlibrary_name: transformers\npipeline_tag: text-generation\nlicense: mit\nlanguage:\n- en\nbase_model:\n- Qwen/Qwen3-30B-A3B-Thinking-2507\ntags:\n- agent\n- open-source\n- miromind\n- deep-research\n---\n\n# <span style=\"color: #7FFF7F;\">MiroThinker-v1.0-30B GGUF Models</span>\n\n\n## <span style=\"color: #7F7FFF;\">Model Generation Details</span>\n\nThis model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`10e978015`](https://github.com/ggerganov/llama.cpp/commit/10e9780154365b191fb43ca4830659ef12def80f).\n\n\n\n\n\n---\n\n## <span style=\"color: #7FFF7F;\">Quantization Beyond the IMatrix</span>\n\nI've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.\n\nIn 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:  \n👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)\n\nWhile this does increase model file size, it significantly improves precision for a given quantization level.\n\n### **I'd love your feedback—have you tried this? How does it perform for you?**\n\n\n\n\n---\n\n<a href=\"https://readyforquantum.com/huggingface_gguf_selection_guide.html\" style=\"color: #7FFF7F;\">\n  Click here to get info on choosing the right GGUF model format\n</a>\n\n---\n\n\n\n<!--Begin Original Model Card-->\n\n\n<div align=\"center\">\n  <img src=\"https://cdn-uploads.huggingface.co/production/uploads/68525b342230a897a65cc1c0/87mYQ_a-4jpnMkVR4hrgm.png\" width=\"55%\" alt=\"MiroThinker\" />\n</div>\n\n<div align=\"center\">\n\n[![Demo](https://img.shields.io/badge/Demo-FFB300?style=for-the-badge&logo=airplayvideo&logoColor=white)](https://dr.miromind.ai/)\n[![Models](https://img.shields.io/badge/Models-5EDDD2?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/collections/miromind-ai/mirothinker-v10)\n[![Paper](https://img.shields.io/badge/Paper-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2511.11793)\n\n[![Github](https://img.shields.io/badge/GitHub-24292F?style=for-the-badge&logo=github&logoColor=white)](https://github.com/MiroMindAI/MiroThinker)\n[![Discord](https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.com/invite/GPqEnkzQZd)\n[![WeChat](https://img.shields.io/badge/WeChat-07C160?style=for-the-badge&logo=wechat&logoColor=white)](https://raw.githubusercontent.com/MiroMindAI/MiroThinker/refs/heads/main/assets/miromind_wechat.png)\n[![RedNote](https://img.shields.io/badge/RedNote-FF2442?style=for-the-badge&logo=revoltdotchat&logoColor=white)](https://www.xiaohongshu.com/user/profile/5e353bd80000000001000239)\n[![Website](https://img.shields.io/badge/Website-4285F4?style=for-the-badge&logo=monster&logoColor=white)](https://miromind.ai/)\n\n</div>\n\n## Introduction\n\nMiroThinker v1.0 is an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. \n\nUnlike previous agents that scale only model size or context length, MiroThinker introduces **interactive scaling** at the model level, systematically training the model to handle deeper and more frequent agent–environment interactions as a third dimension of performance improvement. Interactive scaling leverages environment feedback and external information acquisition to correct errors and refine trajectories. \n\nEmpirical results demonstrate the effectiveness of this interactive scaling. Performance across several benchmarks improves predictably as the model engages in increasingly deep and frequent interactions with its environment.\n\n![image](https://huggingface.co/datasets/miromind-ai/MiroFlow-Benchmarks/resolve/main/assets/MiroThinker_v1.0_Overall.png)\n\n**Key Features**\n\n- MiroThinker v1.0 supports a 256K context window, long-horizon reasoning, and deep multi-step analysis.\n- Handles up to 600 tool calls per task — a substantial improvement over previous open-source research agents.\n- Released in 8B, 30B, and 72B parameter scales, accompanied by a comprehensive suite of tools and workflows to flexibly support diverse research settings and compute budgets.\n\n<div align=\"center\">\n  \n|      Model Name      |         Base Model          | Max Length | Max Tool Calls |                              HF Link                               |\n|:--------------------:|:---------------------------:|:----------:|:--------------:|:------------------------------------------------------------------:|\n| MiroThinker-v1.0-8B  |        Qwen3-8B             |    256K    |      600       | [🤗 link](https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B)  |\n| MiroThinker-v1.0-30B | Qwen3-30B-A3B-Thinking-2507 |    256K    |      600       | [🤗 link](https://huggingface.co/miromind-ai/MiroThinker-v1.0-30B) |\n| MiroThinker-v1.0-72B |    Qwen2.5-72B-Instruct     |    256K    |      600       | [🤗 link](https://huggingface.co/miromind-ai/MiroThinker-v1.0-72B) |\n\n</div>\n\nMiroThinker v1.0 demonstrates strong general-research performance across a broad range of benchmarks, achieving 37.7%, 47.1%, 55.6%, and 81.9% on HLE-Text, BrowseComp, BrowseComp-ZH, and GAIA-Text-103, respectively. These results surpass previous open-source agents and narrow the gap with commercial counterparts such as GPT-5-high.\n\n![image](https://huggingface.co/datasets/miromind-ai/MiroFlow-Benchmarks/resolve/main/assets/MiroThinker_v1.0_Performance_1.png)\n\nMore details can be found in our [technical report](https://arxiv.org/abs/2511.11793).\n\n## Online Demo\n\nWelcome to try out our online demo [here](https://dr.miromind.ai/).\n\n## Performance \n\n> To prevent potential information leakage (e.g., searching benchmark answers from HuggingFace), access to HuggingFace has been explicitly disabled in these tools.\n\n<div>\n  <img src=\"https://huggingface.co/datasets/miromind-ai/MiroFlow-Benchmarks/resolve/main/assets/MiroThinker_v1.0_Performance_2.png\" width=\"100%\" alt=\"MiroThinker\" />\n</div>\n\n## Interactive Scaling\n\n![image](https://cdn-uploads.huggingface.co/production/uploads/68525b342230a897a65cc1c0/35d4Rp63y9NLpT7bwk4Zn.png)\n\nThe RL-tuned MiroThinker-v1.0-30B model exhibits far longer and deeper interaction trajectories than its SFT counterpart across all four major benchmarks. While SFT models often terminate after only a few tool calls, the RL model performs extended multi-turn reasoning, exploring and verifying information before concluding.\n\nThis behavioral shift yields **8–10 points accuracy gains**, showing a clear link between interaction depth and performance. We refer to this effect as **interactive scaling**: increasing the frequency and depth of tool-augmented interactions reliably improves research reasoning capability. This forms a third dimension of scaling—alongside model size and context length—defining MiroThinker’s path toward more general agentic intelligence.\n\n## Quick Start\n\nPlease refer to our GitHub repository for installation instructions, examples, and full documentation:\n\n👉 **[https://github.com/MiroMindAI/MiroThinker](https://github.com/MiroMindAI/MiroThinker)**\n\n## License\n\nMiroThinker v1.0 is released under the MIT License.\n\n## Citation\n\nIf you find this project useful in your research, please consider citing:\n\n```\n@article{miromind2025mirothinker,\n  title={MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling},\n  author={MiroMind Team and Bai, Song and Bing, Lidong and Chen, Carson and Chen, Guanzheng and Chen, Yuntao and Chen, Zhe and Chen, Ziyi and Dai, Jifeng and Dong, Xuan and others},\n  journal={arXiv preprint arXiv:2511.11793},\n  year={2025}\n}\n```\n\n## Contact Us\n\nMiroThinker is developed by the MiroMind AI Team.\nIf you would like to leave us a message, feel free to get in touch. \nIn addition to [GitHub](https://github.com/MiroMindAI/), \n[Discord](https://discord.com/invite/GPqEnkzQZd), \n[WeChat](https://raw.githubusercontent.com/MiroMindAI/MiroThinker/refs/heads/main/assets/miromind_wechat.png), \nand [RedNote](https://www.xiaohongshu.com/user/profile/5e353bd80000000001000239), \nyou can also reach us via email at service@miromind.ai.\n\n<!--End Original Model Card-->\n\n---\n\n# <span id=\"testllm\" style=\"color: #7F7FFF;\">🚀 If you find these models useful</span>\n\nHelp me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  \n\n👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  \n\n\nThe 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](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)\n\n💬 **How to test**:  \n Choose an **AI assistant type**:  \n   - `TurboLLM` (GPT-4.1-mini)  \n   - `HugLLM` (Hugginface Open-source models)  \n   - `TestLLM` (Experimental CPU-only)  \n\n### **What I’m Testing**  \nI’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  \n- **Function calling** against live network services  \n- **How small can a model go** while still handling:  \n  - Automated **Nmap security scans**  \n  - **Quantum-readiness checks**  \n  - **Network Monitoring tasks**  \n\n🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  \n- ✅ **Zero-configuration setup**  \n- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.\n- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  \n\n### **Other Assistants**  \n🟢 **TurboLLM** – Uses **gpt-4.1-mini** :\n- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. \n- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**\n- **Real-time network diagnostics and monitoring**\n- **Security Audits**\n- **Penetration testing** (Nmap/Metasploit)  \n\n🔵 **HugLLM** – Latest Open-source models:  \n- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.\n\n### 💡 **Example commands you could test**:  \n1. `\"Give me info on my websites SSL certificate\"`  \n2. `\"Check if my server is using quantum safe encyption for communication\"`  \n3. `\"Run a comprehensive security audit on my server\"`\n4. '\"Create a cmd processor to .. (what ever you want)\" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!\n\n### Final Word\n\nI 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](https://github.com/Mungert69). Feel free to use whatever you find helpful.\n\nIf you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.\n\nI'm also open to job opportunities or sponsorship.\n\nThank you! 😊\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "agent",
    "open-source",
    "miromind",
    "deep-research",
    "text-generation",
    "en",
    "arxiv:2511.11793",
    "base_model:Qwen/Qwen3-30B-A3B-Thinking-2507",
    "base_model:quantized:Qwen/Qwen3-30B-A3B-Thinking-2507",
    "license:mit",
    "endpoints_compatible",
    "region:us",
    "conversational"
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  "last_modified": "2025-11-19T18:04:46.000Z",
  "created_at": "2025-11-19T01:47:25.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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