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README.md
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---
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base_model: meta-llama/Meta-Llama-3-8B
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tags:
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- generated_from_trainer
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model-index:
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- name: Meta-Llama-3-8B_derta
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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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# Meta-Llama-3-8B-Instruct_derta_100step
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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 [Evol-Instruct](https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k) and [BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) dataset.
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## Model description
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Please refer to the paper [Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training](https://arxiv.org/abs/2407.09121) and GitHub [DeRTa](https://github.com/RobustNLP/DeRTa).
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Input format:
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```
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[INST] Your Instruction [\INST]
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```
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## Intended uses & limitations
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The model is trained with DeRTa, showing a high safety performance.
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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: 2e-05
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- train_batch_size: 16
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- weight_decay: 2e-5
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- eval_batch_size: 1
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- seed: 1
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 8
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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: 2.0
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### Training results
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### Framework versions
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- Transformers 4.40.0
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- Pytorch 2.2.0+cu118
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- Datasets 2.10.0
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- Tokenizers 0.19.1
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