Instructions to use Yan777/trained_weigths with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Yan777/trained_weigths with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "Yan777/trained_weigths") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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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 [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7291
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 8
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.5764 | 1.0 | 755 | 0.6950 |
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| 0.5964 | 2.0 | 1510 | 0.6837 |
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| 0.5912 | 3.0 | 2265 | 0.6830 |
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| 0.6013 | 4.0 | 3020 | 0.6878 |
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| 0.5122 | 5.0 | 3775 | 0.6994 |
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| 0.4787 | 6.0 | 4530 | 0.7133 |
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| 0.4859 | 7.0 | 5285 | 0.7235 |
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| 0.4924 | 8.0 | 6040 | 0.7291 |
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### Framework versions
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adapter_model.safetensors
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runs/Apr09_20-50-53_DESKTOP-5GR7SN9/events.out.tfevents.1712667060.DESKTOP-5GR7SN9.21868.3
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training_args.bin
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