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
File size: 3,525 Bytes
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"status": "pass",
"summary": {
"run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu",
"dataset_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu",
"train_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6",
"eval_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
"backbone": "qwen3_omni_lora",
"backbone_status": "implemented",
"checkpoint_gate": "lora_safetensors_shape_check",
"required_stage": "eval",
"workspace": "<project>",
"manifest": {
"path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu/episode_manifest.json",
"episode_count": 128,
"split_counts": {
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},
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"dataset": {
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"manifest_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset_manifest.json",
"dataset_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl",
"manifest_num_samples": 3808,
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"sample_split_counts": {
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},
"training": {
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"checkpoint_candidates": [
"<project>/checkpoints/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6/adapter_lora",
"<project>/checkpoints/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_lora/adapter_lora"
],
"checkpoint_gate": "lora_safetensors_shape_check",
"required_training_files": [
"training_metadata.json",
"progress.jsonl",
"adapter_config.json",
"adapter_model.safetensors"
],
"checkpoint_dir": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6/adapter_lora",
"num_processes": 8,
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"num_val_samples": 0,
"history_len": 1,
"checkpoint_dir_recorded": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6/adapter_lora"
},
"eval": {
"eval_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
"required_eval_files": [
"metrics.json",
"predictions.jsonl",
"predictions.csv",
"per_class_metrics.csv",
"confusion_matrix.csv",
"RUN_REPORT.md"
],
"eval_split": "test",
"num_eval_episodes": 14,
"held_out_episode_count": null,
"json_validity_rate": 0.8526785714285714,
"action_macro_f1": 0.00213753459655099,
"prediction_file": "predictions.jsonl",
"prediction_rows": 448
}
},
"issues": []
}
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