Meta Llama - CUDA Error
I’m a newbie to this and a newbie to python as well. I have no idea what CUDA is, tried looking up but cannot find any solution.I tried setting up in Pycharm and Windows 11. Already installed Tensorflow 2.0, transformers, torch using pip. I downloaded Llama from huggingface and I get this error when I try to run. I have also installed bitsandbytes through pip.The folder path in my code is set this way -  bot = Llama3(“C:/Users/RAH/PycharmProjects/LLM/meta-llama/”)Could this be a folder path issue or an issue with CUDA? Within the folder “C:/Users/RAH/PycharmProjects/LLM/meta-llama” I have 2 sub folders - Llama-3.3-70B-Instruct and Meta-Llama-3-8B-Instruct. Should I be pointing to one of those folders in the code as opposed to the higher level folder?*** Error Message Below ***File “C:\Users\RAH\PycharmProjects\LLM.venv\Lib\site-packages\transformers\integrations\bitsandbytes.py”, line 537, in _validate_bnb_cuda_backend_availabilityraise RuntimeError(log_msg)RuntimeError: CUDA is required but not available for bitsandbytes. Please consider installing the multi-platform enabled version of bitsandbytes, which is currently a work in progress. Please check currently supported platforms and installation instructions atInstallation Guide
2025-01-22T02:09:54Z
Hi there,It looks like you’re running into an issue with CUDA and thebitsandbyteslibrary, which is causing the error.In this case,bitsandbytesis trying to use CUDA to accelerate certain operations, but it seems like CUDA is not available or configured properly on your machine.Possible Solutions:Check for GPU Support:CUDA is only useful if you have an NVIDIA GPU. If you don’t have an NVIDIA GPU, you’ll need to use the CPU for your tasks, andbitsandbytesmight not be the best library to use for your setup.You can check if your system has an NVIDIA GPU and if CUDA is installed properly by running:import torch
print(torch.cuda.is_available())  # Should return True if CUDA is properly set upIf You Have an NVIDIA GPU:Ensure that you have installed the correct version of CUDA for your GPU and that thetorchandbitsandbyteslibraries are compatible with your CUDA version.You might need to install thebitsandbytesversion that supports multi-platform (including CPU). This can be done by:pip install bitsandbytes-cudaXXX  # Replace 'XXX' with your CUDA versionIf You Don’t Have an NVIDIA GPU or Don’t Need CUDA:You can modify the code to avoid using GPU acceleration by using the CPU instead. In this case, you might not needbitsandbytesat all.In the case of transformers or Llama, you can run them on the CPU by specifying the device as CPU in your code. For example:import torch
device = torch.device('cpu')
model.to(device)Check Folder Path:The folder path seems to be fine, but it’s better to point directly to the subfolders (Llama-3.3-70B-InstructorMeta-Llama-3-8B-Instruct), as those contain the actual model files. For example:bot = Llama3("C:/Users/RAH/PycharmProjects/LLM/meta-llama/Llama-3.3-70B-Instruct")