Robotics
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
vision-language-action
qwen2.5-vl
lora
flock-ai-arena
text-generation-inference
Instructions to use random-sequence/flock-robotics-qwen25vl-vla-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use random-sequence/flock-robotics-qwen25vl-vla-4b with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("random-sequence/flock-robotics-qwen25vl-vla-4b") model = AutoModelForMultimodalLM.from_pretrained("random-sequence/flock-robotics-qwen25vl-vla-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
FLock Robotics VLA - Qwen2.5-VL-3B + action head (~4.06B)
Vision-language-action policy for the FLock AI Arena robotics task. A frozen Qwen2.5-VL-3B-Instruct backbone (LoRA-adapted, merged) with an MLP action head, behaviour-cloned on scripted robosuite Lift trajectories.
flock_robotics_adapter.py exposes load_policy(model_dir, device, dtype) returning a policy whose act(obs) maps an agentview image + instruction to a 7-DoF action. ~4.06B parameters (under the 7B validator cap).
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