google-deepmind / google-deepmind/ai-foundations
Bug: TypeError while loading Gemma-1B model in the lab `gdm_lab_1_3_compare_n_gram_models_and_transformer_language_models.ipynb`
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Description
## Steps to Reproduce:
1. Open the "Compare N-Gram Models and Transformer Language Models" lab in Google Colab.
2. Ensure a GPU runtime is selected (Runtime > Change runtime type > GPU). I was using the T4 GPU free version.
3. Run all cells sequentially until the cell containing `gemma_model = generation.load_gemma()`.
## Error
Here is the cell output:
```
Loaded Africa Galore dataset with 232 paragraphs.
Loaded trigram model.
Loading Gemma-1B model...
WARNING:absl:Provided metadata contains unknown key custom. Adding it to custom_metadata.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[/tmp/ipython-input-2823723241.py](https://github.com/google-deepmind/ai-foundations/issues/new#) in ()
12
13 print("Loading Gemma-1B model...")
---> 14 gemma_model = generation.load_gemma()
15 print("Loaded Gemma-1B model.")
2 frames[/usr/local/lib/python3.12/dist-packages/ai_foundations/generation/loaders.py](https://github.com/google-deepmind/ai-foundations/issues/new#) in load_gemma(model_name)
57 else:
58 model = attention.AttentionWeightGemma3_1B()
---> 59 params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA3_1B_PT)
60 elif model_name == "Gemma-4B":
61 tokenizer = gm.text.Gemma3Tokenizer()
[/usr/local/lib/python3.12/dist-packages/gemma/gm/ckpts/_checkpoint.py](https://github.com/google-deepmind/ai-foundations/issues/new#) in load_params(path, params, donate, text_only, sharding, quantize)
186 ckpt = ocp.StandardCheckpointer()
187
--> 188 metadata, path = _get_metadata_and_path(ckpt, path)
189
190 metadata = _CheckpointTree.shape_dtype_struct_like(tree=metadata)
[/usr/local/lib/python3.12/dist-packages/gemma/gm/ckpts/_checkpoint.py](https://github.com/google-deepmind/ai-foundations/issues/new#) in _get_metadata_and_path(ckpt, path)
431 else:
432 raise
--> 433 metadata = dict(metadata) # Normalize metadata
434 return metadata, path
435
TypeError: 'StepMetadata' object is not iterable
```
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