mirror of
https://github.com/turboderp-org/exllamav2.git
synced 2026-05-11 08:20:29 +00:00
277 lines
8.3 KiB
Python
277 lines
8.3 KiB
Python
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import sys, os, re
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from exllamav2 import(
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ExLlamaV2,
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ExLlamaV2Config,
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ExLlamaV2Cache,
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ExLlamaV2Tokenizer,
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model_init,
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)
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import argparse
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import torch
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from exllamav2.generator import (
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ExLlamaV2StreamingGenerator,
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ExLlamaV2Sampler
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)
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from chat_formatting import CodeBlockFormatter
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from chat_prompts import prompt_formats
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prompt_formats_list = list(prompt_formats.keys())
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import time
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# Options
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parser = argparse.ArgumentParser(description = "Simple Llama2 chat example for ExLlamaV2")
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parser.add_argument("-modes", "--modes", action = "store_true", help = "List available modes and exit.")
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parser.add_argument("-mode", "--mode", choices = prompt_formats_list, help = "Chat mode. Use llama for Llama 1/2 chat finetunes.")
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parser.add_argument("-un", "--username", type = str, default = "User", help = "Username when using raw chat mode")
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parser.add_argument("-bn", "--botname", type = str, default = "Chatbort", help = "Bot name when using raw chat mode")
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parser.add_argument("-sp", "--system_prompt", type = str, help = "Use custom system prompt")
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parser.add_argument("-temp", "--temperature", type = float, default = 0.95, help = "Sampler temperature, default = 0.95 (1 to disable)")
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parser.add_argument("-topk", "--top_k", type = int, default = 50, help = "Sampler top-K, default = 50 (0 to disable)")
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parser.add_argument("-topp", "--top_p", type = float, default = 0.8, help = "Sampler top-P, default = 0.8 (0 to disable)")
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parser.add_argument("-typical", "--typical", type = float, default = 0.0, help = "Sampler typical threshold, default = 0.0 (0 to disable)")
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parser.add_argument("-repp", "--repetition_penalty", type = float, default = 1.1, help = "Sampler repetition penalty, default = 1.1 (1 to disable)")
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parser.add_argument("-maxr", "--max_response_tokens", type = int, default = 1000, help = "Max tokens per response, default = 1000")
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parser.add_argument("-resc", "--response_chunk", type = int, default = 250, help = "Space to reserve in context for reply, default = 250")
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parser.add_argument("-ncf", "--no_code_formatting", action = "store_true", help = "Disable code formatting/syntax highlighting")
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# Arrrgs
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model_init.add_args(parser)
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args = parser.parse_args()
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# Prompt templates/modes
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if args.modes:
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print(" -- Available formats:")
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for k, v in prompt_formats.items():
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print(f" -- {k:12} : {v().description}")
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sys.exit()
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username = args.username
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botname = args.botname
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system_prompt = args.system_prompt
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if args.mode is None:
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print(" ## Error: No mode specified.")
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sys.exit()
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prompt_format = prompt_formats[args.mode]()
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prompt_format.botname = botname
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prompt_format.username = username
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if system_prompt is None: system_prompt = prompt_format.default_system_prompt()
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# Initialize model and tokenizer
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model_init.check_args(args)
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model_init.print_options(args)
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model, tokenizer = model_init.init(args)
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# Create cache
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cache = ExLlamaV2Cache(model)
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# Chat context
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def format_prompt(user_prompt, first):
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global system_prompt, prompt_format
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if first:
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return prompt_format.first_prompt() \
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.replace("<|system_prompt|>", system_prompt) \
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.replace("<|user_prompt|>", user_prompt)
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else:
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return prompt_format.subs_prompt() \
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.replace("<|user_prompt|>", user_prompt)
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def encode_prompt(text):
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global tokenizer, prompt_format
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add_bos, add_eos, encode_special_tokens = prompt_format.encoding_options()
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return tokenizer.encode(text, add_bos = add_bos, add_eos = add_eos, encode_special_tokens = encode_special_tokens)
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user_prompts = []
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responses_ids = []
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def get_tokenized_context(max_len):
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global user_prompts, responses_ids
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while True:
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context = torch.empty((1, 0), dtype=torch.long)
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for turn in range(len(user_prompts)):
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up_text = format_prompt(user_prompts[turn], context.shape[-1] == 0)
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up_ids = encode_prompt(up_text)
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context = torch.cat([context, up_ids], dim=-1)
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if turn < len(responses_ids):
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context = torch.cat([context, responses_ids[turn]], dim=-1)
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if context.shape[-1] < max_len: return context
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# If the context is too long, remove the first Q/A pair and try again. The system prompt will be moved to
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# the first entry in the truncated context
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user_prompts = user_prompts[1:]
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responses_ids = responses_ids[1:]
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# Generator
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generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)
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settings = ExLlamaV2Sampler.Settings()
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settings.temperature = args.temperature
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settings.top_k = args.top_k
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settings.top_p = args.top_p
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settings.typical = args.typical
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settings.token_repetition_penalty = args.repetition_penalty
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max_response_tokens = args.max_response_tokens
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min_space_in_context = args.response_chunk
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# Stop conditions
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generator.set_stop_conditions(prompt_format.stop_conditions(tokenizer))
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# ANSI color codes
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col_default = "\u001b[0m"
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col_user = "\u001b[33;1m" # Yellow
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col_bot = "\u001b[34;1m" # Blue
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col_error = "\u001b[31;1m" # Magenta
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col_sysprompt = "\u001b[37;1m" # Grey
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# Code block formatting
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codeblock_formatter = None if args.no_code_formatting else CodeBlockFormatter()
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in_code_block = False
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delim_overflow = ""
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# Main loop
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print(f" -- Prompt format: {args.mode}")
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print(f" -- System prompt:")
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print()
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print(col_sysprompt + system_prompt.strip() + col_default)
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while True:
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# Get user prompt
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print()
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up = input(col_user + username + ": " + col_default).strip()
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print()
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# Add to context
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user_prompts.append(up)
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# Send tokenized context to generator
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active_context = get_tokenized_context(model.config.max_seq_len - min_space_in_context)
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generator.begin_stream(active_context, settings)
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# Stream response
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if prompt_format.print_bot_name():
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print(col_bot + botname + ": " + col_default, end = "")
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response_tokens = 0
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response_text = ""
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responses_ids.append(torch.empty((1, 0), dtype = torch.long))
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while True:
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# Get response stream
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chunk, eos, tokens = generator.stream()
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if len(response_text) == 0: chunk = chunk.lstrip()
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response_text += chunk
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responses_ids[-1] = torch.cat([responses_ids[-1], tokens], dim = -1)
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# Check for code block delimiters
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# Let formatter suppress text as long as it may be part of delimiter
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chunk, codeblock_delimiter = (chunk, False) if codeblock_formatter is None else codeblock_formatter.process_delimiter(chunk)
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# Enter code block
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if not in_code_block:
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# Start of codeblock
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if codeblock_delimiter:
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codeblock_formatter.begin()
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print("\n")
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in_code_block = True
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codeblock_delimiter = False
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# Print
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if in_code_block:
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# Print unformatted
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codeblock_formatter.print_code_block(chunk)
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else:
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# Print formatted
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print(chunk, end = "")
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# Exit code block
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if in_code_block:
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# End of code block
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if codeblock_delimiter:
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# Edge case when we get EOS right after code block
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if eos: codeblock_formatter.print_code_block("\n")
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print("\033[0m") # Reset block color to be certain
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in_code_block = False
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codeblock_delimiter = False
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sys.stdout.flush()
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# time.sleep(1)
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# If model has run out of space, rebuild the context and restart stream
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if generator.full():
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active_context = get_tokenized_context(model.config.max_seq_len - min_space_in_context)
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generator.begin_stream(active_context, settings)
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# If response is too long, cut it short, and append EOS if that was a stop condition
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response_tokens += 1
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if response_tokens == max_response_tokens:
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if tokenizer.eos_token_id in generator.stop_tokens:
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responses_ids[-1] = torch.cat([responses_ids[-1], tokenizer.single_token(tokenizer.eos_token_id)], dim = -1)
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print()
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print(col_error + f" !! Response exceeded {max_response_tokens} tokens and was cut short." + col_default)
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break
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# EOS signal returned
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if eos:
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if prompt_format.print_extra_newline():
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print()
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break
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