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Add a tokenizer placeholder
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@ -21,8 +21,11 @@ def get_max_prompt_length(tokens):
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return max_length
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return max_length
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def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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# These models do not have explicit tokenizers for now, so
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# we return an estimate on the number of tokens
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if shared.is_RWKV or shared.is_LLaMA:
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if shared.is_RWKV or shared.is_LLaMA:
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return prompt
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return np.zeros((1, len(prompt)//5))
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
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if shared.args.cpu:
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if shared.args.cpu:
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