Instructions to use stefanj0/t5gemma-math-corrector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use stefanj0/t5gemma-math-corrector with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5gemma-s-s-ul2-it") model = PeftModel.from_pretrained(base_model, "stefanj0/t5gemma-math-corrector") - Transformers
How to use stefanj0/t5gemma-math-corrector with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stefanj0/t5gemma-math-corrector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -5
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README.md
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This model is a fine-tuned version of [google/t5gemma-s-s-ul2-it](https://huggingface.co/google/t5gemma-s-s-ul2-it) on the None dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [google/t5gemma-s-s-ul2-it](https://huggingface.co/google/t5gemma-s-s-ul2-it) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.8249
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.0085 | 0.6415 | 200 | 0.6018 |
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| 0.0029 | 1.2823 | 400 | 1.9704 |
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| 0.001 | 1.9238 | 600 | 3.8351 |
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| 0.0005 | 2.5646 | 800 | 3.8249 |
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### Framework versions
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adapter_model.safetensors
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