lora_Meta-Llama-3-70B_derta
This model is a fine-tuned version of meta-llama/Meta-Llama-3-70B-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 LLaMA3-70B-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
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Model tree for Youliang/llama3-70b-instruct-lora-derta-100step
Base model
meta-llama/Meta-Llama-3-70B
Finetuned
meta-llama/Meta-Llama-3-70B-Instruct