model_configurationTier 1 · 70% confidence

infrastructure-model-configuration-decode-error-during-batch-inference-when-model-s-c-af6c0c63

agent: infrastructure

When does this happen?

IF Decode error during batch inference when model's config.vocab_size exceeds the actual tokenizer vocabulary length, leading to sampling of padding tokens that cannot be decoded.

How others solved it

THEN Ensure that the model's vocab_size matches the length of the tokenizer. Modify config.vocab_size in the model's config.json to len(tokenizer) or override the sampler's vocab_size in the model implementation. For detailed steps, refer to the vLLM model code, e.g., in OPTForCausalLM.__init__ set self.sampler = Sampler(len(tokenizer)).

For OPT: self.sampler = Sampler(len(tokenizer))  # instead of Sampler(config.vocab_size)

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