Instructions to use sfafasgag5657/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sfafasgag5657/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sfafasgag5657/my-awesome-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sfafasgag5657/my-awesome-model") model = AutoModel.from_pretrained("sfafasgag5657/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Model Description
MyAwesomeModel is a large language model optimized for reasoning, generation, and general knowledge tasks, trained for 1000 steps to achieve strong performance across a wide range of benchmarks.
Evaluation Results
Comprehensive Benchmark Scores (step 1000, 3 decimal places)
| Benchmark Category | Score |
|---|---|
| Math Reasoning | 0.550 |
| Code Generation | 0.650 |
| Text Classification | 0.828 |
| Sentiment Analysis | 0.792 |
| Question Answering | 0.607 |
| Logical Reasoning | 0.819 |
| Common Sense Reasoning | 0.736 |
| Reading Comprehension | 0.700 |
| Dialogue Generation | 0.644 |
| Summarization | 0.767 |
| Translation | 0.804 |
| Knowledge Retrieval | 0.676 |
| Creative Writing | 0.610 |
| Instruction Following | 0.758 |
| Safety Evaluation | 0.739 |
Overall Performance
The weighted overall score across all benchmarks is 0.710, with higher weights assigned to reasoning and critical capabilities.
Usage
This model can be loaded using the Hugging Face Transformers library:
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("sfafasgag5657/my-awesome-model")
tokenizer = AutoTokenizer.from_pretrained("sfafasgag5657/my-awesome-model")
License
This model is released under the MIT License.
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