Fabulous, thanks so much! So, it runs, but now there’s an error at the end of the generation process:
/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/base_model.py:40: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  checkpoint = torch.load(cached_file, map_location="cpu")
Traceback (most recent call last):
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/gradio/queueing.py", line 536, in process_events
    response = await route_utils.call_process_api(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/gradio/route_utils.py", line 322, in call_process_api
    output = await app.get_blocks().process_api(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/gradio/blocks.py", line 1935, in process_api
    result = await self.call_function(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/gradio/blocks.py", line 1520, in call_function
    prediction = await anyio.to_thread.run_sync(  # type: ignore
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2441, in run_sync_in_worker_thread
    return await future
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 943, in run
    result = context.run(func, *args)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/gradio/utils.py", line 826, in wrapper
    response = f(*args, **kwargs)
  File "/home/user/app/utils/generate_synthetic.py", line 252, in launch_main
    prompt_str = model_blip.generate({"image": _image})[0]
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/blip_models/blip_caption.py", line 188, in generate
    decoder_out = self.text_decoder.generate_from_encoder(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 1360, in generate_from_encoder
    outputs = self.generate(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/transformers/generation/utils.py", line 2246, in generate
    result = self._beam_search(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/transformers/generation/utils.py", line 3455, in _beam_search
    outputs = self(**model_inputs, return_dict=True)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 1210, in forward
    outputs = self.bert(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 974, in forward
    encoder_outputs = self.encoder(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 592, in forward
    layer_outputs = layer_module(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 475, in forward
    cross_attention_outputs = self.crossattention(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 346, in forward
    self_outputs = self.self(
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/.pyenv/versions/3.10.15/lib/python3.10/site-packages/lavis/models/med.py", line 219, in forward
    attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
RuntimeError: The size of tensor a (3) must match the size of tensor b (9) at non-singleton dimension 0
Do you have a fork where it runs out of the box? Wondering if it’s because of the cache I have in mine… Don’t wory about this, though, as mentioned I’m unfortunately not in a position to work on this at the moment 
 …
Congrats for solving the dependency maze!