Instructions to use liuferet114/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liuferet114/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liuferet114/MyAwesomeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liuferet114/MyAwesomeModel") model = AutoModel.from_pretrained("liuferet114/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Overview
MyAwesomeModel has undergone a significant version upgrade, showing significant improvements in depth of reasoning and inference capabilities through increased computational resources and algorithmic optimization during post-training. The model demonstrates outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic.
Evaluation Results
Comprehensive Benchmark Results (All 15 Benchmarks)
Below are the detailed evaluation results for the best checkpoint (step_1000):
| Category | Benchmark | MyAwesomeModel Score |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 | |
| Common Sense | 0.736 | |
| Language Understanding | Reading Comprehension | 0.700 |
| Question Answering | 0.607 | |
| Text Classification | 0.828 | |
| Sentiment Analysis | 0.792 | |
| Generation Tasks | Code Generation | 0.650 |
| Creative Writing | 0.610 | |
| Dialogue Generation | 0.644 | |
| Summarization | 0.767 | |
| Specialized Capabilities | Translation | 0.804 |
| Knowledge Retrieval | 0.676 | |
| Instruction Following | 0.758 | |
| Safety Evaluation | 0.739 |
Overall Performance Summary
- Weighted Overall Score: 0.710
- The model shows particular strength in reasoning and understanding tasks.
- The weighted scoring system gives higher weight to core reasoning tasks (math: 1.2x, logic: 1.2x) and code generation (1.1x).
How to Use
This model follows the standard Transformers BertModel interface.
License
This model is licensed under the MIT License.
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