Instructions to use safafaf22/MyAwesomeModel-step1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use safafaf22/MyAwesomeModel-step1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safafaf22/MyAwesomeModel-step1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("safafaf22/MyAwesomeModel-step1000") model = AutoModel.from_pretrained("safafaf22/MyAwesomeModel-step1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel (step_1000)
This repository holds the best checkpoint of MyAwesomeModel, selected from the
workspace training run (checkpoints/step_1000). The selection was made by running
the workspace's own evaluation/eval.py pipeline across all 10 checkpoints
(step_100 … step_1000); step_1000 achieved the highest weighted overall score.
Selected checkpoint
- Checkpoint:
step_1000 - Weighted overall score:
0.712(highest among all checkpoints)
Evaluation results — all 15 benchmarks (step_1000)
Scores are reported to 3 decimal places.
| # | Benchmark | Score |
|---|---|---|
| 1 | math_reasoning | 0.550 |
| 2 | code_generation | 0.650 |
| 3 | text_classification | 0.828 |
| 4 | sentiment_analysis | 0.792 |
| 5 | question_answering | 0.607 |
| 6 | logical_reasoning | 0.819 |
| 7 | common_sense | 0.736 |
| 8 | reading_comprehension | 0.700 |
| 9 | dialogue_generation | 0.644 |
| 10 | summarization | 0.767 |
| 11 | translation | 0.804 |
| 12 | knowledge_retrieval | 0.676 |
| 13 | creative_writing | 0.610 |
| 14 | instruction_following | 0.758 |
| 15 | safety_evaluation | 0.739 |
Weighted overall score
The overall score is the weighted average of the 15 benchmark scores, using the
weights defined in evaluation/eval.py (reasoning tasks receive extra weight):
- math_reasoning ×1.2, logical_reasoning ×1.2
- code_generation ×1.1, question_answering ×1.1, instruction_following ×1.1, safety_evaluation ×1.1
- reading_comprehension ×1.0, common_sense ×1.0, dialogue_generation ×1.0, summarization ×1.0, translation ×1.0, knowledge_retrieval ×1.0
- text_classification ×0.9, sentiment_analysis ×0.9, creative_writing ×0.9
Weighted overall = 0.712.
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