Instructions to use Ritual-Net/answer-emojis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ritual-Net/answer-emojis with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Ritual-Net/answer-emojis") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +15 -1
- adapter_model.bin +3 -0
README.md
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ritualnah/ppml/runs/3q9smy0v)
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# answer-emojis
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on
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## Model description
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 3
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### Framework versions
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- PEFT 0.11.1
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ritualnah/ppml/runs/3q9smy0v)
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# answer-emojis
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5239
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## Model description
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.0155 | 0.0082 | 1 | 1.2302 |
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| 0.5161 | 0.5031 | 61 | 0.5744 |
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| 0.5398 | 1.0062 | 122 | 0.5379 |
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| 0.4614 | 1.4990 | 183 | 0.5295 |
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| 0.4323 | 2.0021 | 244 | 0.5178 |
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| 0.3823 | 2.4948 | 305 | 0.5239 |
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### Framework versions
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- PEFT 0.11.1
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:85c4faae61bbc825e0cc2cb4fffec4970bbf8e5b964b2234454c6447ab66e1fa
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size 1368620762
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