The previous attempt to fix the "Cannot read properties of null
(reading 'dims')" chat error was incomplete: I only stopped passing
the bogus return_tensor:'pt' option to apply_chat_template. The
underlying issue was that apply_chat_template's all-in-one mode
(return_dict:true) does not produce a proper Tensor-backed
{ input_ids, attention_mask } pair for multimodal-capable processors
like Gemma4Processor — it returns a shape that has no .dims on
input_ids, so model.generate() crashes deep inside the forward pass
the moment it tries to read the sequence length.
Switch to the documented two-step pattern from the Gemma 4 model
card: call apply_chat_template with tokenize:false to get the
formatted prompt as a plain string, then run that string through
processor.tokenizer with return_tensors:'pt' to get a proper Tensor
pair. The tokenizer's return_tensors option is the *Python*
convention and IS supported by transformers.js's Tokenizer class
(the API name collision between apply_chat_template's return_tensor
boolean and Tokenizer's return_tensors string is one of those nasty
spots where the JS port intentionally diverges from Python).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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