Upload nvidia_Nemotron-Cascade-14B-Thinking_0.txt with huggingface_hub

nvidia_Nemotron-Cascade-14B-Thinking_0.txt

@@ -0,0 +1,53 @@
+```CODE: 
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="nvidia/Nemotron-Cascade-14B-Thinking")
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe(messages)
+```
+
+ERROR: 
+Traceback (most recent call last):
+  File "/tmp/nvidia_Nemotron-Cascade-14B-Thinking_0Uk9fdc.py", line 26, in <module>
+    pipe = pipeline("text-generation", model="nvidia/Nemotron-Cascade-14B-Thinking")
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/transformers/pipelines/__init__.py", line 1229, in pipeline
+    return pipeline_class(model=model, framework=framework, task=task, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/transformers/pipelines/text_generation.py", line 121, in __init__
+    super().__init__(*args, **kwargs)
+    ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/transformers/pipelines/base.py", line 1044, in __init__
+    self.model.to(self.device)
+    ~~~~~~~~~~~~~^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/transformers/modeling_utils.py", line 4343, in to
+    return super().to(*args, **kwargs)
+           ~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1371, in to
+    return self._apply(convert)
+           ~~~~~~~~~~~^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 930, in _apply
+    module._apply(fn)
+    ~~~~~~~~~~~~~^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 930, in _apply
+    module._apply(fn)
+    ~~~~~~~~~~~~~^^^^
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 930, in _apply
+    module._apply(fn)
+    ~~~~~~~~~~~~~^^^^
+  [Previous line repeated 2 more times]
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 957, in _apply
+    param_applied = fn(param)
+  File "/tmp/.cache/uv/environments-v2/d389e4950376e589/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1357, in convert
+    return t.to(
+           ~~~~^
+        device,
+        ^^^^^^^
+        dtype if t.is_floating_point() or t.is_complex() else None,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        non_blocking,
+        ^^^^^^^^^^^^^
+    )
+    ^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 170.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 14.69 MiB is free. Process 25878 has 22.28 GiB memory in use. Of the allocated memory 22.04 GiB is allocated by PyTorch, and 1.32 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)