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eo1-qwen2_5_vl
IPEC-COMMUNITY/eo1-qwen2_5_vl
eo1-qwen2_5_vl is a machine learning model from IPEC-COMMUNITY. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
A pre-initialized vision-language-action model based on Qwen2.5-VL-3B-Instruct, specifically designed for recent Lerobot PR: https://github.com/huggingface/lerobot/pull/1971
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From the Hugging Face model README
EO-1 Vision-Language-Action Model (Initialization)
A pre-initialized vision-language-action model based on Qwen2.5-VL-3B-Instruct, specifically designed for recent Lerobot PR: https://github.com/huggingface/lerobot/pull/1971
🚀 Quick Start
from transformers import AutoProcessor, AutoModelForCausalLM
# Load the model and processor
model = AutoModelForCausalLM.from_pretrained("IPEC-COMMUNITY/eo1-qwen2_5_vl-initial", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("IPEC-COMMUNITY/eo1-qwen2_5_vl-initial", trust_remote_code=True)
# Ready for training - no additional setup required!
🎯 Key Features
- Pre-configured Special Tokens: All EO-1 robotic tokens are pre-added to the vocabulary
- Multimodal Processing: Integrated processor handles images, videos, text, robot states, and actions
- Training-Ready: Directly loadable for fine-tuning without modifications
- Based on Qwen2.5-VL-3B: Inherits strong vision-language understanding capabilities
🔧 Special Tokens
The model includes pre-configured special tokens for robotic manipulation:
| Token | Purpose |
|---|---|
<|action_start|> | Marks the beginning of action sequences |
<|action_pad|> | Padding token for actions |
<|action_pass|> | Pass-through token for actions |
<|action_end|> | Marks the end of action sequences |
<|state_start|> | Marks the beginning of state sequences |
<|state_pad|> | Padding token for states |
<|state_end|> | Marks the end of state sequences |
<|vla|> | Vision-Language-Action task token |
📊 Data Processing
The integrated processor handles multiple modalities:
- Images: Automatically resized to adaptive pixels
- Videos: Automatically resized to adaptive pixels
- Text: Standard tokenization with special token support
- Robot States: Vectorized and tokenized
- Actions: Vectorized and tokenized with denoising support
🏗️ Model Architecture
- Base Model: Qwen2.5-VL-3B-Instruct
- Vision Encoder: Pre-trained vision transformer
- Language Model: 3B parameter transformer
- Action Projector: Custom layers for robotic action prediction
- Flow Matching: Integrated denoising mechanism for action generation
💡 Usage Project
- 🤗Lerobot: https://github.com/huggingface/lerobot/tree/main/src/lerobot/policies/eo1
- 🚀EO-1: https://github.com/EO-Robotics/EO-1
🤝 Contributing
For issues, questions, or contributions, please visit our GitHub repository.
Note: This is an initialization model. For best results, fine-tune on your specific robotic task data.
- Type
- eo1
- License
- mit
- Languages
- en
- Created
- Sep 18, 2025
- Updated
- Sep 20, 2025