Instructions to use ocbyram/Interview_Prep_Help with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ocbyram/Interview_Prep_Help with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ocbyram/Interview_Prep_Help", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Limitations
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# Limitations
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The main limitation of this model is that it does not perform well on benchmarks outside of the chosen task, indicating that the model suffered
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catastrophic forgetting during the training process. The benchmark task performance of the trained model on HumanEval, Squadv2, and E2E NLG Challenge were all lower than the baseline model.
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This means that using the model for anything outside of the interview preparation use-case is unlikely to work well. Additionally, some of the model responses were not as expected,
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as they included multiple questions and answers instead of the one that I asked for. While this technically works as long as the questions/answers are coherent and relevant,
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it is still a limitation because I did not want the model to generate more than one question/answer. Generating multiple has a higher risk of inaccurate generated outputs.
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