Instructions to use petra345/MyAwesomeModel-GateRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/MyAwesomeModel-GateRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/MyAwesomeModel-GateRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/MyAwesomeModel-GateRepo") model = AutoModel.from_pretrained("petra345/MyAwesomeModel-GateRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_name": "MyAwesomeModel-GateRepo", | |
| "selection_rule": "earliest checkpoint with weighted_overall_score >= 0.650 and safety_evaluation >= 0.700", | |
| "gate_thresholds": { | |
| "weighted_overall_score_min": 0.650, | |
| "safety_evaluation_min": 0.700 | |
| }, | |
| "selected_checkpoint": "step_600", | |
| "first_passing_checkpoint": "step_600", | |
| "weighted_overall_score": 0.656, | |
| "selected_model_sha256": "965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587", | |
| "benchmark_scores": { | |
| "math_reasoning": 0.487, | |
| "logical_reasoning": 0.675, | |
| "common_sense": 0.690, | |
| "reading_comprehension": 0.645, | |
| "question_answering": 0.575, | |
| "text_classification": 0.776, | |
| "sentiment_analysis": 0.762, | |
| "code_generation": 0.577, | |
| "creative_writing": 0.534, | |
| "dialogue_generation": 0.596, | |
| "summarization": 0.725, | |
| "translation": 0.780, | |
| "knowledge_retrieval": 0.643, | |
| "instruction_following": 0.717, | |
| "safety_evaluation": 0.707 | |
| }, | |
| "gate_checks": { | |
| "weighted_overall_score": 0.656, | |
| "safety_evaluation": 0.707, | |
| "passed": true | |
| }, | |
| "evaluated_checkpoints": [ | |
| { | |
| "checkpoint": "step_100", | |
| "weighted_overall_score": 0.480, | |
| "safety_evaluation": 0.628, | |
| "gate_passed": false | |
| }, | |
| { | |
| "checkpoint": "step_200", | |
| "weighted_overall_score": 0.535, | |
| "safety_evaluation": 0.650, | |
| "gate_passed": false | |
| }, | |
| { | |
| "checkpoint": "step_300", | |
| "weighted_overall_score": 0.576, | |
| "safety_evaluation": 0.668, | |
| "gate_passed": false | |
| }, | |
| { | |
| "checkpoint": "step_400", | |
| "weighted_overall_score": 0.608, | |
| "safety_evaluation": 0.683, | |
| "gate_passed": false | |
| }, | |
| { | |
| "checkpoint": "step_500", | |
| "weighted_overall_score": 0.635, | |
| "safety_evaluation": 0.696, | |
| "gate_passed": false | |
| }, | |
| { | |
| "checkpoint": "step_600", | |
| "weighted_overall_score": 0.656, | |
| "safety_evaluation": 0.707, | |
| "gate_passed": true | |
| }, | |
| { | |
| "checkpoint": "step_700", | |
| "weighted_overall_score": 0.674, | |
| "safety_evaluation": 0.717, | |
| "gate_passed": true | |
| }, | |
| { | |
| "checkpoint": "step_800", | |
| "weighted_overall_score": 0.689, | |
| "safety_evaluation": 0.725, | |
| "gate_passed": true | |
| }, | |
| { | |
| "checkpoint": "step_900", | |
| "weighted_overall_score": 0.700, | |
| "safety_evaluation": 0.732, | |
| "gate_passed": true | |
| }, | |
| { | |
| "checkpoint": "step_1000", | |
| "weighted_overall_score": 0.710, | |
| "safety_evaluation": 0.739, | |
| "gate_passed": true | |
| } | |
| ] | |
| } | |