Robotics
PyTorch
Cosmos
xperience10m_task_baseline_suite
embodied-ai
multimodal
xperience-10m
baseline
evaluation
qwen3-omni
Instructions to use cy0307/ropedia-xperience-10m-task-baselines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use cy0307/ropedia-xperience-10m-task-baselines with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Qwen3-Omni LoRA Pipeline Figure Provenance
This presentation figure was generated with ChatGPT image generation and then committed as a public visual asset for the Ropedia Xperience-10M task-suite project.
Figure file:
docs/assets/qwen3_omni_lora_pipeline.png
Design brief:
- Show the end-to-end flow from valid Xperience-10M raw episodes into episode-level split validation, parallel media and sensor export, Qwen3-Omni-compatible JSONL records, video/audio/text inputs, sensor-bridge features, LoRA adapter training, and final outputs.
- Make clear that train/validation staging and held-out test evaluation are separate steps.
- Use the dark green Ropedia-style visual language already used by the website.
- Do not show raw private data, model weights, or unverifiable metrics.
Scope:
- The figure documents the prepared Qwen3-Omni LoRA training path.
- It is not a completed held-out training metric report.