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@@ -170,10 +170,10 @@ def generate_reply(question, tokens, inference_settings, selected_model, eos_tok
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cuda = "" if args.cpu else ".cuda()"
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cuda = "" if args.cpu else ".cuda()"
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n = None if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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n = None if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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+ input_ids = encode(question, tokens)
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# Generate the entire reply at once
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# Generate the entire reply at once
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if args.no_stream:
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if args.no_stream:
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- input_ids = encode(question, tokens)
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output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
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output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
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reply = decode(output[0])
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reply = decode(output[0])
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yield formatted_outputs(reply, model_name)
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yield formatted_outputs(reply, model_name)
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@@ -181,7 +181,6 @@ def generate_reply(question, tokens, inference_settings, selected_model, eos_tok
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# Generate the reply 1 token at a time
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# Generate the reply 1 token at a time
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else:
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else:
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yield formatted_outputs(question, model_name)
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yield formatted_outputs(question, model_name)
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- input_ids = encode(question, 1)
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preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=1')
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preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=1')
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for i in tqdm(range(tokens)):
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for i in tqdm(range(tokens)):
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output = eval(f"model.generate(input_ids, {preset}){cuda}")
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output = eval(f"model.generate(input_ids, {preset}){cuda}")
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