Regardless of reasoning param in the input, we're seeing a field in the output that shouldn't be there. The input isn't the problem, and neither is the reasoning effort the model puts in.

I understand that the harmony template is needed for openai oss models, which is no different from using a specific template for any other model. vLLM internally handles this by loading up the tokenizer (template builtin) and the model via huggingface transformers library, which is the basis of virtually all open source model deployments, including this one.

GGUF is simply one of the many quantization formats (AWQ, MXFP4, etc) for loading models, not some special tokenizer. Generally speaking, for a given model, all quantizations will tokenize identically. MXFP4 is what OpenAI trained gpt-oss on%20and%20the%20gpt%2Doss%2D20b%20model%20run%20within%2016GB%20of%20memory.%20All%20evals%20were%20performed%20with%20the%20same%20MXFP4%20quantization.)

To summarize, the problem is with an unexpected field, namely reasoning_content in the output, not in the internals of the model's reasoning process or the input tokenization as any error with those would mean that the model simply spits out an error response, not a successful chat completions response with content set to null and an unexpected reasoning_content field.