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# 论文训练参数+正常推理参数
### model
model_name_or_path: Qwen/Qwen2.5-Coder-14B-Instruct
trust_remote_code: true
### method
stage: sft
do_train: true
finetuning_type: full
deepspeed: /home/xuye_liu/yubo/LLaMA-Factory/examples/deepspeed/ds_z3_config.json
### dataset
dataset: r2egym_sft_trajectories
dataset_dir: /home/xuye_liu/yubo/LLaMA-Factory/data
template: qwen
cutoff_len: 20000
max_samples: 100000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: /home/xuye_liu/yubo/LLaMA-Factory/saves/R2EGym-14B-Agent-Coder-Instruct
logging_steps: 10
resume_from_checkpoint: null
save_steps: 200
plot_loss: true
overwrite_output_dir: false
### train
flash_attn: fa2
enable_liger_kernel: true
use_unsloth_gc: true
per_device_train_batch_size: 1
# Global batch size = per_device_train_batch_size * gradient_accumulation_steps * world_size.
# Using GPUs 4,5,6,7 => world_size=4, so 1 * 2 * 4 = 8.
gradient_accumulation_steps: 2
learning_rate: 1.0e-5
weight_decay: 0.05
num_train_epochs: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### wandb
report_to: none
run_name: R2EGym-14B-Agent-Coder
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