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use SOTA sampling for GPT2

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lvmin 2023-09-13 18:33:14 -07:00
parent 1a088db0eb
commit 1964aec7f8
1 changed files with 4 additions and 2 deletions
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6
modules/expansion.py
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@ -50,9 +50,11 @@ class FooocusExpansion:
tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device) tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device)
tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device) tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device)
features = self.model.generate(**tokenized_kwargs, num_beams=5, do_sample=True, max_new_tokens=256) # https://huggingface.co/blog/introducing-csearch
response = self.tokenizer.batch_decode(features, skip_special_tokens=True) # https://huggingface.co/docs/transformers/generation_strategies
features = self.model.generate(**tokenized_kwargs, penalty_alpha=0.8, top_k=8, max_new_tokens=256)
response = self.tokenizer.batch_decode(features, skip_special_tokens=True)
result = response[0][len(origin):] result = response[0][len(origin):]
result = safe_str(result) result = safe_str(result)
result = remove_pattern(result, dangrous_patterns) result = remove_pattern(result, dangrous_patterns)