Instructions to use petra345/MyAwesomeModel-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/MyAwesomeModel-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/MyAwesomeModel-TestRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/MyAwesomeModel-TestRepo") model = AutoModel.from_pretrained("petra345/MyAwesomeModel-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_repo": "MyAwesomeModel-TestRepo", | |
| "selected_checkpoint": "checkpoints/step_1000", | |
| "selection_rule": "highest weighted eval_accuracy; tie-breaker highest numeric step", | |
| "weighted_eval_accuracy": 0.710, | |
| "benchmark_groups": [ | |
| { | |
| "group": "Core Reasoning Tasks", | |
| "benchmarks": [ | |
| { | |
| "name": "Math Reasoning", | |
| "key": "math_reasoning", | |
| "score": 0.550, | |
| "weight": 1.2 | |
| }, | |
| { | |
| "name": "Logical Reasoning", | |
| "key": "logical_reasoning", | |
| "score": 0.819, | |
| "weight": 1.2 | |
| }, | |
| { | |
| "name": "Common Sense", | |
| "key": "common_sense", | |
| "score": 0.736, | |
| "weight": 1.0 | |
| } | |
| ], | |
| "group_average": 0.702 | |
| }, | |
| { | |
| "group": "Language Understanding", | |
| "benchmarks": [ | |
| { | |
| "name": "Reading Comprehension", | |
| "key": "reading_comprehension", | |
| "score": 0.700, | |
| "weight": 1.0 | |
| }, | |
| { | |
| "name": "Question Answering", | |
| "key": "question_answering", | |
| "score": 0.607, | |
| "weight": 1.1 | |
| }, | |
| { | |
| "name": "Text Classification", | |
| "key": "text_classification", | |
| "score": 0.828, | |
| "weight": 0.9 | |
| }, | |
| { | |
| "name": "Sentiment Analysis", | |
| "key": "sentiment_analysis", | |
| "score": 0.792, | |
| "weight": 0.9 | |
| } | |
| ], | |
| "group_average": 0.732 | |
| }, | |
| { | |
| "group": "Generation Tasks", | |
| "benchmarks": [ | |
| { | |
| "name": "Code Generation", | |
| "key": "code_generation", | |
| "score": 0.650, | |
| "weight": 1.1 | |
| }, | |
| { | |
| "name": "Creative Writing", | |
| "key": "creative_writing", | |
| "score": 0.610, | |
| "weight": 0.9 | |
| }, | |
| { | |
| "name": "Dialogue Generation", | |
| "key": "dialogue_generation", | |
| "score": 0.644, | |
| "weight": 1.0 | |
| }, | |
| { | |
| "name": "Summarization", | |
| "key": "summarization", | |
| "score": 0.767, | |
| "weight": 1.0 | |
| } | |
| ], | |
| "group_average": 0.668 | |
| }, | |
| { | |
| "group": "Specialized Capabilities", | |
| "benchmarks": [ | |
| { | |
| "name": "Translation", | |
| "key": "translation", | |
| "score": 0.804, | |
| "weight": 1.0 | |
| }, | |
| { | |
| "name": "Knowledge Retrieval", | |
| "key": "knowledge_retrieval", | |
| "score": 0.676, | |
| "weight": 1.0 | |
| }, | |
| { | |
| "name": "Instruction Following", | |
| "key": "instruction_following", | |
| "score": 0.758, | |
| "weight": 1.1 | |
| }, | |
| { | |
| "name": "Safety Evaluation", | |
| "key": "safety_evaluation", | |
| "score": 0.739, | |
| "weight": 1.1 | |
| } | |
| ], | |
| "group_average": 0.744 | |
| } | |
| ], | |
| "artifact_sha256": { | |
| "config.json": "7087b54618ddc9cd146c068edaae90d07ca5227b4a0d9bdc9f54e3c03d4dcd39", | |
| "pytorch_model.bin": "965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587", | |
| "figures/fig1.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a", | |
| "figures/fig2.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a", | |
| "figures/fig3.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a" | |
| } | |
| } | |