Create README.md

README.md

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+---
+base_model: meta-llama/Meta-Llama-3-8B
+tags:
+- generated_from_trainer
+model-index:
+- name: Meta-Llama-3-8B_derta
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Meta-Llama-3-8B-Instruct_derta_100step
+
+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.
+
+## Model description
+
+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).
+
+Input format:
+```
+[INST] Your Instruction [\INST]
+```
+## Intended uses & limitations
+
+The model is trained with DeRTa, showing a high safety performance.
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- weight_decay: 2e-5
+- eval_batch_size: 1
+- seed: 1
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 128
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 2.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.2.0+cu118
+- Datasets 2.10.0
+- Tokenizers 0.19.1