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---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-70B
model-index:
- name: lora_Meta-Llama-3-70B_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. -->
# lora_Meta-Llama-3-70B_derta
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) 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).
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 1
- seed: 1
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2.0
The lora config is:
```
{
"lora_r": 96,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_modules": [
"q_proj",
"v_proj",
"k_proj",
"o_proj",
"gate_proj",
"down_proj",
"up_proj",
"w1",
"w2",
"w3"
]
}
```
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.0+cu118
- Datasets 2.10.0
- Tokenizers 0.19.1 |