Collecting unsloth
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Collecting unsloth_zoo>=2025.8.5 (from unsloth)
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Collecting tyro (from unsloth)
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Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=2.4.0->unsloth)
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Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch>=2.4.0->unsloth)
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Collecting nvidia-cufft-cu12==11.3.3.83 (from torch>=2.4.0->unsloth)
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Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch>=2.4.0->unsloth)
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Collecting nvidia-nvtx-cu12==12.8.90 (from torch>=2.4.0->unsloth)
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Collecting nvidia-cufile-cu12==1.13.1.3 (from torch>=2.4.0->unsloth)
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Installing collected packages: nvidia-cusparselt-cu12, triton, sympy, shtab, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, tyro, nvidia-cusolver-cu12, torch, datasets, xformers, torchvision, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth
Attempting uninstall: nvidia-cusparselt-cu12
Found existing installation: nvidia-cusparselt-cu12 0.6.2
Uninstalling nvidia-cusparselt-cu12-0.6.2:
Successfully uninstalled nvidia-cusparselt-cu12-0.6.2
Attempting uninstall: triton
Found existing installation: triton 3.2.0
Uninstalling triton-3.2.0:
Successfully uninstalled triton-3.2.0
Attempting uninstall: sympy
Found existing installation: sympy 1.13.1
Uninstalling sympy-1.13.1:
Successfully uninstalled sympy-1.13.1
Attempting uninstall: nvidia-nvtx-cu12
Found existing installation: nvidia-nvtx-cu12 12.4.127
Uninstalling nvidia-nvtx-cu12-12.4.127:
Successfully uninstalled nvidia-nvtx-cu12-12.4.127
Attempting uninstall: nvidia-nvjitlink-cu12
Found existing installation: nvidia-nvjitlink-cu12 12.5.82
Uninstalling nvidia-nvjitlink-cu12-12.5.82:
Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82
Attempting uninstall: nvidia-nccl-cu12
Found existing installation: nvidia-nccl-cu12 2.23.4
Uninstalling nvidia-nccl-cu12-2.23.4:
Successfully uninstalled nvidia-nccl-cu12-2.23.4
Attempting uninstall: nvidia-curand-cu12
Found existing installation: nvidia-curand-cu12 10.3.6.82
Uninstalling nvidia-curand-cu12-10.3.6.82:
Successfully uninstalled nvidia-curand-cu12-10.3.6.82
Attempting uninstall: nvidia-cuda-runtime-cu12
Found existing installation: nvidia-cuda-runtime-cu12 12.5.82
Uninstalling nvidia-cuda-runtime-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82
Attempting uninstall: nvidia-cuda-nvrtc-cu12
Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82
Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82
Attempting uninstall: nvidia-cuda-cupti-cu12
Found existing installation: nvidia-cuda-cupti-cu12 12.5.82
Uninstalling nvidia-cuda-cupti-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82
Attempting uninstall: nvidia-cublas-cu12
Found existing installation: nvidia-cublas-cu12 12.5.3.2
Uninstalling nvidia-cublas-cu12-12.5.3.2:
Successfully uninstalled nvidia-cublas-cu12-12.5.3.2
Attempting uninstall: nvidia-cusparse-cu12
Found existing installation: nvidia-cusparse-cu12 12.5.1.3
Uninstalling nvidia-cusparse-cu12-12.5.1.3:
Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3
Attempting uninstall: nvidia-cufft-cu12
Found existing installation: nvidia-cufft-cu12 11.2.3.61
Uninstalling nvidia-cufft-cu12-11.2.3.61:
Successfully uninstalled nvidia-cufft-cu12-11.2.3.61
Attempting uninstall: nvidia-cudnn-cu12
Found existing installation: nvidia-cudnn-cu12 9.3.0.75
Uninstalling nvidia-cudnn-cu12-9.3.0.75:
Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75
Attempting uninstall: nvidia-cusolver-cu12
Found existing installation: nvidia-cusolver-cu12 11.6.3.83
Uninstalling nvidia-cusolver-cu12-11.6.3.83:
Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83
Attempting uninstall: torch
Found existing installation: torch 2.6.0+cu124
Uninstalling torch-2.6.0+cu124:
Successfully uninstalled torch-2.6.0+cu124
Attempting uninstall: datasets
Found existing installation: datasets 4.0.0
Uninstalling datasets-4.0.0:
Successfully uninstalled datasets-4.0.0
Attempting uninstall: torchvision
Found existing installation: torchvision 0.21.0+cu124
Uninstalling torchvision-0.21.0+cu124:
Successfully uninstalled torchvision-0.21.0+cu124
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
fastai 2.7.19 requires torch<2.7,>=1.10, but you have torch 2.8.0 which is incompatible.
torchaudio 2.6.0+cu124 requires torch==2.6.0, but you have torch 2.8.0 which is incompatible.
Successfully installed bitsandbytes-0.47.0 cut_cross_entropy-25.1.1 datasets-3.6.0 msgspec-0.19.0 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.3 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvtx-cu12-12.8.90 shtab-1.7.2 sympy-1.14.0 torch-2.8.0 torchvision-0.23.0 triton-3.4.0 trl-0.21.0 tyro-0.9.28 unsloth-2025.8.6 unsloth_zoo-2025.8.5 xformers-0.0.32.post2
Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.11/dist-packages (0.47.0)
ERROR: Could not find a version that satisfies the requirement unsloth_zo (from versions: none)
ERROR: No matching distribution found for unsloth_zo
colab卸载torch 2.8.0，安装torch==2.6.0
在Google Colab中卸载并安装特定版本的PyTorch，可以通过以下步骤完成。首先，我们将卸载当前版本的PyTorch，然后安装所需版本的PyTorch。
步骤 1: 卸载当前版本的PyTorch
在Google Colab中，你可以使用!pip命令来运行pip命令，以卸载PyTorch。打开你的Colab笔记本，然后运行以下代码来卸载当前的PyTorch版本：
这里，-y参数表示自动确认卸载，无需手动确认。
步骤 2: 安装指定版本的PyTorch
安装特定版本的PyTorch（例如2.6.0），你可以使用以下命令。根据你的需求（例如是否需要CUDA支持），你可以选择合适的命令。例如，如果你需要安装支持CUDA的PyTorch 2.6.0版本，可以使用：
这里，cu113表示这是支持CUDA 11.3的版本。你需要根据你的GPU环境和CUDA版本选择合适的后缀。例如，如果你的环境是CUDA 10.2，则应该使用cu102作为后缀。例如：
检查安装
安装完成后，你可以通过以下Python代码来检查PyTorch的版本：
这将会输出你刚刚安装的PyTorch版本号，确保它是2.6.0。
注意：
-
确保你选择了与你的Colab环境兼容的CUDA版本。你可以在Colab的“Runtime”菜单下选择“Change runtime type”，然后在“Hardware accelerator”选项中选择合适的GPU类型（例如“GPU”或“Tesla T4”等），这有助于确定你需要哪个CUDA版本。
-
如果你的环境中不需要CUDA（即使用CPU），你可以简单地运行：
这将安装不包含CUDA支持的PyTorch版本。
检查colab中CUDA版本
!/opt/bin/nvidia-smi 和 torch.version.cuda 查得的cuda版本不一样。
当你安装 PyTorch 时，它是针对特定版本的 CUDA Toolkit 进行编译的。如果你的 PyTorch 是编译时基于 CUDA Toolkit 11.7 版本，那么torch.version.cuda就会输出11.7，这个输出准确地反映了 PyTorch 所依赖的 CUDA Toolkit 版本。
nvidia - smi
nvidia - smi输出的是显卡驱动的相关信息，其中包括该显卡驱动所支持的最高 CUDA 版本。例如，显卡驱动可能支持最高 CUDA 12.0 版本，但你在系统中实际安装并用于 PyTorch 的 CUDA Toolkit 可能是 11.7 版本。这是因为即使显卡驱动支持更高的 CUDA 版本，你依然可以选择安装和使用较低版本的 CUDA Toolkit 来配合你的深度学习框架和应用程序。所以nvidia - smi输出的 CUDA 版本相关信息可能大于（即支持更高版本）实际安装并用于特定框架（如 PyTorch）的 CUDA Toolkit 版本。
原文链接：https://blog.csdn.net/weixin_43501408/article/details/146635787