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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
inputs = tokenizer(["The secret to baking a good cake is "], return_tensors="pt")
streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
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docs/source/en/generation_features.md
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from transformers import AutoTokenizer, AutoModelForCausalLM, WatermarkDetector, WatermarkingConfig
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = "left"
inputs = tokenizer(["This is the beginning of a long story", "Alice and Bob are"], padding=True, return_tensors="pt")
input_len = inputs["input_ids"].shape[-1]
watermarking_config = WatermarkingConfig(bias=2.5, seeding_scheme="selfhash")
out = model.generate(**inputs, watermarking_config=watermarking_config, do_sample=False, max_length=20)
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docs/source/en/generation_features.md
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detector = WatermarkDetector(model_config=model.config, device="cpu", watermarking_config=watermarking_config)
detection_out = detector(out, return_dict=True)
detection_out.prediction
array([True, True])
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docs/source/en/generation_features.md
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