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Youliang/
llama3-8b-instruct-lora-derta-100step

Name: llama3-8b-instruct-lora-derta-100step
Availability: InStock

safetensors

2407.09121

meta-llama/Meta-Llama-3-8B-Instruct

finetune

apache-2.0

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lora_Meta-Llama-3-8B_derta

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the Evol-Instruct and BeaverTails dataset.

Model description

Please refer to the paper Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training and GitHub DeRTa. The model is continued train 100 steps with DeRTa on LLaMA3-8B-Instruct.

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

Youliang/llama3-8b-instruct-lora-derta-100step