Instructions to use Cccccz/HY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cccccz/HY with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cccccz/HY", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download confidence_validation/runner.log from Cccccz/HY: direct link, hf CLI and curl.
- Browser
- Download file 6.72 kB
-
https://huggingface.co/Cccccz/HY/resolve/main/confidence_validation/runner.log
- Command line
-
hf download hf://Cccccz/HY/confidence_validation/runner.log
-
curl -L -o runner.log https://huggingface.co/Cccccz/HY/resolve/main/confidence_validation/runner.log
6.72 kB
| { | |
| "output": "/mnt/local_nvme/cz/projects/HY-WorldPlay-DEV-Predictor/datasets/hyworldplay_predictor_confidence_validation25_prefeature/cases.jsonl", | |
| "cases": 25 | |
| } | |
| { | |
| "records": 800, | |
| "train_records": 700 | |
| } | |
| WARNING:__main__: | |
| ***************************************** | |
| Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| ***************************************** | |
| rank0 extracted 25/133 batches | |
| rank0 extracted 50/133 batches | |
| rank0 extracted 75/133 batches | |
| rank0 extracted 100/133 batches | |
| rank0 extracted 125/133 batches | |
| Saved 8 feature shards to /mnt/local_nvme/cz/projects/HY-WorldPlay-DEV-Predictor/confidence_validation/features/train | |
| WARNING:__main__: | |
| ***************************************** | |
| Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| ***************************************** | |
| rank0 extracted 25/35 batches | |
| Saved 8 feature shards to /mnt/local_nvme/cz/projects/HY-WorldPlay-DEV-Predictor/confidence_validation/features/validation | |
| WARNING:__main__: | |
| ***************************************** | |
| Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| ***************************************** | |
| [rank0]:[W819 05:29:45.641755152 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank6]:[W819 05:29:45.820996456 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 6] using GPU 6 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank1]:[W819 05:29:45.868540764 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank2]:[W819 05:29:46.322682030 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank7]:[W819 05:29:46.386567270 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 7] using GPU 7 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank5]:[W819 05:29:46.398470481 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 5] using GPU 5 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank4]:[W819 05:29:46.426298692 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 4] using GPU 4 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| [rank3]:[W819 05:29:46.471978908 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id. | |
| c0 epoch 1/40 loss=1.951573 | |
| c0 epoch 10/40 loss=0.075422 | |
| c0 epoch 20/40 loss=0.035323 | |
| c0 epoch 30/40 loss=0.025954 | |
| c0 epoch 40/40 loss=0.020488 | |
| c1 epoch 1/40 loss=3.383299 | |
| c1 epoch 10/40 loss=0.149253 | |
| c1 epoch 20/40 loss=0.046591 | |
| c1 epoch 30/40 loss=0.029699 | |
| c1 epoch 40/40 loss=0.022912 | |
| c2 epoch 1/40 loss=2.855274 | |
| c2 epoch 10/40 loss=0.109034 | |
| c2 epoch 20/40 loss=0.040014 | |
| c2 epoch 30/40 loss=0.029119 | |
| c2 epoch 40/40 loss=0.022536 | |
| { | |
| "protocol": "WorldPlay fulltrain100 (8400 transitions); independent validation25 (2100 transitions); no validation tuning", | |
| "train_samples": 8400, | |
| "validation_samples": 2100, | |
| "c1_validation_metrics": { | |
| "overall": { | |
| "model": "Confidence C1", | |
| "split": "validation", | |
| "step": "overall", | |
| "spearman": 0.9529179491248462, | |
| "pearson": 0.96539306640625, | |
| "log_mae": 0.15999889373779297 | |
| }, | |
| "P1": { | |
| "model": "Confidence C1", | |
| "split": "validation", | |
| "step": "P1", | |
| "spearman": 0.8590718873187204, | |
| "pearson": 0.8829622268676758, | |
| "log_mae": 0.17691564559936523 | |
| }, | |
| "P2": { | |
| "model": "Confidence C1", | |
| "split": "validation", | |
| "step": "P2", | |
| "spearman": 0.8631690385373965, | |
| "pearson": 0.9074602127075195, | |
| "log_mae": 0.14503394067287445 | |
| }, | |
| "P3": { | |
| "model": "Confidence C1", | |
| "split": "validation", | |
| "step": "P3", | |
| "spearman": 0.8700319930976419, | |
| "pearson": 0.8872363567352295, | |
| "log_mae": 0.15804709494113922 | |
| } | |
| }, | |
| "remaining_risk_at_33pct": { | |
| "Random": 0.009558672141283751, | |
| "Timestep-only": 0.005005753133445978, | |
| "Confidence C0": 0.008430036716163158, | |
| "Confidence C1": 0.004878982435911894, | |
| "Confidence C2": 0.004925357177853584, | |
| "Oracle": 0.004840569104999304 | |
| }, | |
| "gain_vs_random": 0.48957529207015615, | |
| "oracle_gap": 0.007935705508866102, | |
| "routing_at_33pct": { | |
| "P1": { | |
| "selection_fraction": 0.012857142857142857, | |
| "within_step_fallback_rate": 0.012857142857142857 | |
| }, | |
| "P2": { | |
| "selection_fraction": 0.05714285714285714, | |
| "within_step_fallback_rate": 0.05714285714285714 | |
| }, | |
| "P3": { | |
| "selection_fraction": 0.93, | |
| "within_step_fallback_rate": 0.93 | |
| } | |
| } | |
| } | |