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qwen2.5-32b微调算力

时间: 2025-02-23 13:55:32 浏览: 176

### Qwen2.5-32B Model Fine-Tuning Computational Resources Required For the supervision fine-tuning of Qwen2.5-32B-Instruct using s1K, a specific set of computational resources was utilized to achieve efficient and effective training outcomes. The process leveraged PyTorch Fully Sharded Data Parallel (FSDP), which is designed to optimize memory usage across multiple GPUs by sharding both model parameters and optimizer states[^1]. The hardware configuration included: - **GPU Count**: Utilization involved 16 NVIDIA H100 GPUs. - **Training Time**: The entire fine-tuning procedure took approximately 26 minutes. This setup demonstrates that leveraging advanced parallelism techniques like FSDP can significantly reduce the time needed for large-scale models such as Qwen2.5-32B while maintaining high performance standards during the training phase. Additionally, it's worth noting that similar efforts with other distilled versions of this architecture have also been successful on different benchmarks, indicating robustness in adapting these models through various optimization strategies including but not limited to hardware selection and software framework choices[^2]. ```python import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from transformers import TrainerCallback class FSDFineTuneTrainer(TrainerCallback): def __init__(self, *args, fsdp=True, **kwargs): super().__init__(*args, **kwargs) self.fsdp = fsdp # Additional methods would be implemented here based on requirements ```

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