Upload train_r2egym_14B_agent_coder_instruct.yaml

train_r2egym_14B_agent_coder_instruct.yaml

@@ -0,0 +1,49 @@
+# 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