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swift sft examples/train/multi-node/ray/sft.yaml
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model: Qwen/Qwen2.5-7B-Instruct
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split_dataset_ratio: 0.0
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tuner_type: lora
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target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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torch_dtype: bfloat16
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attn_impl: flash_attn
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num_train_epochs: 5
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per_device_train_batch_size: 1
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per_device_eval_batch_size: 1
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learning_rate: 1e-4
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dataset: swift/self-cognition#1000
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gradient_accumulation_steps: 8
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eval_steps: 1000
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save_steps: 1000
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save_total_limit: 5
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logging_steps: 5
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warmup_ratio: 0.05
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dataloader_num_workers: 0
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dataset_num_proc: 8
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deepspeed: zero3
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model_name: swift-bot
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model_author: swift
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use_ray: true
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device_groups:
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nproc_per_node: 4
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default:
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device: GPU
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ranks: list(range(0, 4))
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workers:
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- default
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