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--- |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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library_name: peft |
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license: llama3 |
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tags: |
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- trl |
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- kto |
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- generated_from_trainer |
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model-index: |
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- name: kto-aligned-model-lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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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/pauld/huggingface/runs/em482maw) |
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# kto-aligned-model-lora |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5001 |
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- Eval/rewards/chosen: 0.1739 |
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- Eval/logps/chosen: -0.4029 |
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- Eval/rewards/rejected: 0.1787 |
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- Eval/logps/rejected: -0.0087 |
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- Eval/rewards/margins: -0.0048 |
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- Eval/kl: 1.7305 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 12 |
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- total_train_batch_size: 12 |
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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: 0.1 |
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- num_epochs: 5 |
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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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| 0.5 | 1.0 | 9 | 0.5000 | 1.2364 | |
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| 0.4994 | 2.0 | 18 | 0.5002 | 1.7169 | |
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| 0.4985 | 3.0 | 27 | 0.5003 | 1.7311 | |
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| 0.4981 | 4.0 | 36 | 0.5002 | 1.7306 | |
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| 0.4976 | 5.0 | 45 | 0.5001 | 1.7305 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
