Create qwen2_7b_lora_sft_4bit.yaml

llama-factory/config/qwen2_7b_lora_sft_4bit.yaml

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+### model
+model_name_or_path: Qwen/Qwen2-7B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+quantization_bit: 4                     # use 4-bit QLoRA
+loraplus_lr_ratio: 16.0                 # use LoRA+ with lambda=16.0
+# use_unsloth: true                       # use UnslothAI's LoRA optimization for 2x faster training
+
+### dataset
+dataset: alpaca_mac
+template: chatml
+cutoff_len: 1024
+max_samples: 50
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: /content/qwen2-7b/
+logging_steps: 10
+save_steps: 10
+plot_loss: true
+overwrite_output_dir: true
+# resume_from_checkpoint: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 6.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+val_size: 0.02
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 10
+
+report_to: wandb
+run_name: qwen2_7b_mac_colab # optional