RoboBrain2.0-7B GGUF Models
Model Generation Details
This model was generated using
llama.cpp at commit
6adc3c3e.
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.cppWhile 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?
Click here to get info on choosing the right GGUF model format
RoboBrain 2.0: See Better. Think Harder. Do Smarter.
🎯
RoboOS: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain.
🌍
RoboBrain 1.0: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
🔥 Overview
We are excited to introduce RoboBrain 2.0, the most powerful open-source embodied brain model to date. Compared to its predecessor, RoboBrain1.0, our latest version significantly advances multi-agent task planning, spatial reasoning, and closed-loop execution. A detailed technical report will be released soon.
🗞️ News
2025-06-07: 🎉 We highlight the training framework (FlagScale) developed by BAAI Framework R&D team, and the evaluation framework (FlagEvalMM) by BAAI FlagEval team. Both are used for RoboBrain 2.0.2025-06-06: 🤗 RoboBrain 2.0-7B model checkpoint has been released in Huggingface..2025-06-06: 🔥 We're excited to announce the release of our more powerful RoboBrain 2.0.2025-04-11: 🎉 RoboBrain 1.0 was selected for CVPR 2025's official Embodied AI Trends Commentary.2025-02-27: 🌍 RoboBrain 1.0 was accepted to CVPR2025.
📆 Todo
🚀 Features
RoboBrain 2.0 supports interactive reasoning with long-horizon planning and closed-loop feedback, spatial perception for precise point and bbox prediction from complex instructions, temporal perception for future trajectory estimation, and scene reasoning through real-time structured memory construction and update.
⭐️ Architecture
RoboBrain 2.0 supports multi-image, long video, and high-resolution visual inputs, along with complex task instructions and structured scene graphs on the language side. Visual inputs are processed via a Vision Encoder and MLP Projector, while textual inputs are tokenized into a unified token stream. All inputs are fed into a LLM Decoder that performs long-chain-of-thought reasoning and outputs structured plans, spatial relations, and both relative and absolute coordinates.
🤗 Model Zoo
🛠️ Setup
1# clone repo.
2git clone https://github.com/FlagOpen/RoboBrain2.0.git
3cd RoboBrain
4
5# build conda env.
6conda create -n robobrain2 python=3.10
7conda activate robobrain2
8pip install -r requirements.txt
🤖 Simple Inference
Note: Please refer to RoboBrain 2.0 Github for the usage of RoboBrain 2.0
😊 More Results
Benchmark comparison across spatial reasoning and task planning. RoboBrain2.0-32B achieves state-of-the-art performance on four key embodied intelligence benchmarks: BLINK-Spatial, CV-Bench, EmbSpatial, and RefSpatial. It not only outperforms leading open-source models such as o4-mini and Qwen2.5-VL, but also surpasses closed-source models like Gemini 2.5 Pro and Claude Sonnet 4 — especially in the challenging RefSpatial benchmark, where RoboBrain2.0 shows a >50% absolute improvement.
📑 Citation
If you find this project useful, welcome to cite us.
1@article{RoboBrain 2.0 Technical Report,
2 title={RoboBrain 2.0 Technical Report},
3 author={BAAI RoboBrain Team},
4 journal={arXiv preprint arXiv:TODO},
5 year={2025}
6}
7
8@article{RoboBrain 1.0,
9 title={Robobrain: A unified brain model for robotic manipulation from abstract to concrete},
10 author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others},
11 journal={arXiv preprint arXiv:2502.21257},
12 year={2025}
13}
14
15@article{RoboOS,
16 title={RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration},
17 author={Tan, Huajie and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Lyu, Yaoxu and Cao, Mingyu and Wang, Zhongyuan and Zhang, Shanghang},
18 journal={arXiv preprint arXiv:2505.03673},
19 year={2025}
20}
21
22@article{zhou2025roborefer,
23 title={RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics},
24 author={Zhou, Enshen and An, Jingkun and Chi, Cheng and Han, Yi and Rong, Shanyu and Zhang, Chi and Wang, Pengwei and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and others},
25 journal={arXiv preprint arXiv:2506.04308},
26 year={2025}
27}
28
29@article{Reason-RFT,
30 title={Reason-rft: Reinforcement fine-tuning for visual reasoning},
31 author={Tan, Huajie and Ji, Yuheng and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Wang, Zhongyuan and Zhang, Shanghang},
32 journal={arXiv preprint arXiv:2503.20752},
33 year={2025}
34}
35
36@article{Code-as-Monitor,
37 title={Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection},
38 author={Zhou, Enshen and Su, Qi and Chi, Cheng and Zhang, Zhizheng and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and Wang, He},
39 journal={arXiv preprint arXiv:2412.04455},
40 year={2024}
41}
🚀 If you find these models useful
Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:
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:
"Give me info on my websites SSL certificate""Check if my server is using quantum safe encyption for communication""Run a comprehensive security audit on my server"- '"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! 😊
