LLaMA-Factory/examples/extras/qoft/llama3_oft_sft_gptq.yaml
Zeju Qiu 842595698b
[feature] adding orthogononal finetuning (OFT) to llama factory (#8623)
Co-authored-by: Zeju <zqiu@g003.internal.cluster.is.localnet>
Co-authored-by: Zeju <zqiu@login2.is.localnet>
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
2025-08-18 18:22:47 +08:00

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YAML

### model
model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-GPTQ
trust_remote_code: true
### method
stage: sft
do_train: true
finetuning_type: oft
oft_block_size: 32
oft_target: all
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 2048
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4
### output
output_dir: saves/llama3-8b/oft/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: false
report_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow]
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### eval
# val_size: 0.1
# per_device_eval_batch_size: 1
# eval_strategy: steps
# eval_steps: 500