---
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: []
license: apache-2.0
---

<!-- 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).

Input format:
```
[INST] Your Instruction [\INST]
```
## 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: 8
- 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: 8
- 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