Instructions to use petra345/RobustAwesomeModel-RiskFloorRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/RobustAwesomeModel-RiskFloorRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/RobustAwesomeModel-RiskFloorRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/RobustAwesomeModel-RiskFloorRepo") model = AutoModel.from_pretrained("petra345/RobustAwesomeModel-RiskFloorRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_name": "RobustAwesomeModel-RiskFloorRepo", | |
| "selected_checkpoint": "checkpoints/step_800", | |
| "required_files": [ | |
| "README.md", | |
| "config.json", | |
| "pytorch_model.bin", | |
| "figures/fig1.png", | |
| "figures/fig2.png", | |
| "figures/fig3.png", | |
| "risk_report.json", | |
| "decision_table.csv", | |
| "group_summary.csv", | |
| "lineage_audit.csv", | |
| "release_trace.zip", | |
| "release_evidence.tar", | |
| "release_integrity.json", | |
| "upload_manifest.json" | |
| ], | |
| "forbidden_paths": [ | |
| "checkpoint_metrics.csv", | |
| "benchmark_results.csv", | |
| "checkpoints/", | |
| "evaluation/" | |
| ], | |
| "file_sha256_12": { | |
| "README.md": "901ece9ff6e0", | |
| "config.json": "fb4107be640e", | |
| "pytorch_model.bin": "01934db10231", | |
| "figures/fig1.png": "bd81e62dbd42", | |
| "figures/fig2.png": "bd81e62dbd42", | |
| "figures/fig3.png": "bd81e62dbd42", | |
| "risk_report.json": "043309ec3016", | |
| "decision_table.csv": "1680bb71347f", | |
| "group_summary.csv": "cd5792dd5dc7", | |
| "lineage_audit.csv": "972560e8be76", | |
| "release_trace.zip": "bec02644ae63", | |
| "release_evidence.tar": "1ef0a7a61bf9", | |
| "release_integrity.json": "034d7d44600f", | |
| "upload_manifest.json": "self-referential" | |
| }, | |
| "readback_required": true | |
| } | |