203 lines
9.6 KiB
Python
203 lines
9.6 KiB
Python
model_name = "InternLM"
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cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
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from transformers import AutoModel, AutoTokenizer
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import time
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import threading
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import importlib
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from toolbox import update_ui, get_conf, ProxyNetworkActivate
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from multiprocessing import Process, Pipe
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from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
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# ------------------------------------------------------------------------------------------------------------------------
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# 🔌💻 Local Model Utils
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# ------------------------------------------------------------------------------------------------------------------------
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def try_to_import_special_deps():
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import sentencepiece
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def combine_history(prompt, hist):
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user_prompt = "<|User|>:{user}<eoh>\n"
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robot_prompt = "<|Bot|>:{robot}<eoa>\n"
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cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"
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messages = hist
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total_prompt = ""
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for message in messages:
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cur_content = message
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cur_prompt = user_prompt.replace("{user}", cur_content[0])
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total_prompt += cur_prompt
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cur_prompt = robot_prompt.replace("{robot}", cur_content[1])
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total_prompt += cur_prompt
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total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
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return total_prompt
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# ------------------------------------------------------------------------------------------------------------------------
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# 🔌💻 Local Model
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# ------------------------------------------------------------------------------------------------------------------------
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class GetInternlmHandle(LocalLLMHandle):
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def load_model_info(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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self.model_name = model_name
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self.cmd_to_install = cmd_to_install
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def try_to_import_special_deps(self, **kwargs):
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"""
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import something that will raise error if the user does not install requirement_*.txt
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"""
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import sentencepiece
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def load_model_and_tokenizer(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = get_conf('LOCAL_MODEL_DEVICE')
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with ProxyNetworkActivate('Download_LLM'):
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if self._model is None:
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tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
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if device=='cpu':
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model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
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else:
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model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
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model = model.eval()
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return model, tokenizer
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def llm_stream_generator(self, **kwargs):
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import torch
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import logging
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import copy
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import warnings
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import torch.nn as nn
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from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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def adaptor():
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model = self._model
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tokenizer = self._tokenizer
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prompt = kwargs['query']
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max_length = kwargs['max_length']
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top_p = kwargs['top_p']
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temperature = kwargs['temperature']
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history = kwargs['history']
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real_prompt = combine_history(prompt, history)
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return model, tokenizer, real_prompt, max_length, top_p, temperature
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model, tokenizer, prompt, max_length, top_p, temperature = adaptor()
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prefix_allowed_tokens_fn = None
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logits_processor = None
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stopping_criteria = None
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additional_eos_token_id = 103028
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generation_config = None
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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# 🏃♂️🏃♂️🏃♂️ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25
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inputs = tokenizer([prompt], padding=True, return_tensors="pt")
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input_length = len(inputs["input_ids"][0])
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device = get_conf('LOCAL_MODEL_DEVICE')
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for k, v in inputs.items():
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inputs[k] = v.to(device)
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input_ids = inputs["input_ids"]
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batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
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if generation_config is None:
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generation_config = model.generation_config
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generation_config = copy.deepcopy(generation_config)
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model_kwargs = generation_config.update(**kwargs)
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bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
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if isinstance(eos_token_id, int):
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eos_token_id = [eos_token_id]
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if additional_eos_token_id is not None:
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eos_token_id.append(additional_eos_token_id)
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has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
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if has_default_max_length and generation_config.max_new_tokens is None:
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warnings.warn(
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f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
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"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
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" recommend using `max_new_tokens` to control the maximum length of the generation.",
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UserWarning,
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)
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elif generation_config.max_new_tokens is not None:
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generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
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if not has_default_max_length:
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logging.warn(
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f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
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f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
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"Please refer to the documentation for more information. "
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"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
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UserWarning,
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)
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if input_ids_seq_length >= generation_config.max_length:
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input_ids_string = "input_ids"
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logging.warning(
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f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
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f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
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" increasing `max_new_tokens`."
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)
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# 2. Set generation parameters if not already defined
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logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
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logits_processor = model._get_logits_processor(
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generation_config=generation_config,
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input_ids_seq_length=input_ids_seq_length,
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encoder_input_ids=input_ids,
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
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logits_processor=logits_processor,
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)
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stopping_criteria = model._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=stopping_criteria
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)
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logits_warper = model._get_logits_warper(generation_config)
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unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
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scores = None
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while True:
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model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
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# forward pass to get next token
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outputs = model(
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**model_inputs,
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return_dict=True,
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output_attentions=False,
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output_hidden_states=False,
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)
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next_token_logits = outputs.logits[:, -1, :]
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# pre-process distribution
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next_token_scores = logits_processor(input_ids, next_token_logits)
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next_token_scores = logits_warper(input_ids, next_token_scores)
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# sample
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probs = nn.functional.softmax(next_token_scores, dim=-1)
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if generation_config.do_sample:
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next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
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else:
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next_tokens = torch.argmax(probs, dim=-1)
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# update generated ids, model inputs, and length for next step
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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model_kwargs = model._update_model_kwargs_for_generation(
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outputs, model_kwargs, is_encoder_decoder=False
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)
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unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
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output_token_ids = input_ids[0].cpu().tolist()
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output_token_ids = output_token_ids[input_length:]
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for each_eos_token_id in eos_token_id:
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if output_token_ids[-1] == each_eos_token_id:
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output_token_ids = output_token_ids[:-1]
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response = tokenizer.decode(output_token_ids)
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yield response
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# stop when each sentence is finished, or if we exceed the maximum length
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if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
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return
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# ------------------------------------------------------------------------------------------------------------------------
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# 🔌💻 GPT-Academic Interface
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# ------------------------------------------------------------------------------------------------------------------------
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predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetInternlmHandle, model_name) |