mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 16:17:57 +01:00
Sort some imports
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parent
365b672531
commit
f0fcd1f697
@ -14,8 +14,11 @@ import modules.shared as shared
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from modules.extensions import apply_extensions
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from modules.extensions import apply_extensions
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from modules.html_generator import chat_html_wrapper, make_thumbnail
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from modules.html_generator import chat_html_wrapper, make_thumbnail
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from modules.logging_colors import logger
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from modules.logging_colors import logger
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from modules.text_generation import (generate_reply, get_encoded_length,
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from modules.text_generation import (
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get_max_prompt_length)
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generate_reply,
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get_encoded_length,
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get_max_prompt_length
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)
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from modules.utils import delete_file, replace_all, save_file
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from modules.utils import delete_file, replace_all, save_file
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@ -8,8 +8,10 @@ from tqdm import tqdm
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from modules import shared
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from modules import shared
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from modules.models import load_model, unload_model
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from modules.models import load_model, unload_model
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from modules.models_settings import (get_model_settings_from_yamls,
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from modules.models_settings import (
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update_model_parameters)
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get_model_settings_from_yamls,
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update_model_parameters
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)
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from modules.text_generation import encode
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from modules.text_generation import encode
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@ -1,9 +1,3 @@
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'''
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This is a library for formatting text outputs as nice HTML.
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'''
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import os
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import os
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import re
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import re
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import time
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import time
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@ -7,9 +7,15 @@ from pathlib import Path
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import torch
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import torch
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import transformers
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import transformers
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from accelerate import infer_auto_device_map, init_empty_weights
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from accelerate import infer_auto_device_map, init_empty_weights
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from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
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from transformers import (
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AutoModelForSeq2SeqLM, AutoTokenizer,
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AutoConfig,
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BitsAndBytesConfig, LlamaTokenizer)
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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LlamaTokenizer
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)
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import modules.shared as shared
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import modules.shared as shared
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from modules import llama_attn_hijack, sampler_hijack
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from modules import llama_attn_hijack, sampler_hijack
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@ -21,8 +27,10 @@ transformers.logging.set_verbosity_error()
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local_rank = None
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local_rank = None
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if shared.args.deepspeed:
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if shared.args.deepspeed:
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import deepspeed
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import deepspeed
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from transformers.deepspeed import (HfDeepSpeedConfig,
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from transformers.deepspeed import (
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is_deepspeed_zero3_enabled)
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HfDeepSpeedConfig,
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is_deepspeed_zero3_enabled
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)
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from modules.deepspeed_parameters import generate_ds_config
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from modules.deepspeed_parameters import generate_ds_config
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@ -7,10 +7,14 @@ sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit")))
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import autograd_4bit
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import autograd_4bit
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from amp_wrapper import AMPWrapper
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from amp_wrapper import AMPWrapper
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from autograd_4bit import (Autograd4bitQuantLinear,
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from autograd_4bit import (
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load_llama_model_4bit_low_ram)
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Autograd4bitQuantLinear,
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load_llama_model_4bit_low_ram
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)
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from monkeypatch.peft_tuners_lora_monkey_patch import (
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from monkeypatch.peft_tuners_lora_monkey_patch import (
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Linear4bitLt, replace_peft_model_with_gptq_lora_model)
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Linear4bitLt,
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replace_peft_model_with_gptq_lora_model
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)
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from modules import shared
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from modules import shared
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from modules.GPTQ_loader import find_quantized_model_file
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from modules.GPTQ_loader import find_quantized_model_file
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@ -3,9 +3,11 @@ import math
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import torch
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import torch
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import transformers
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import transformers
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from transformers import LogitsWarper
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from transformers import LogitsWarper
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from transformers.generation.logits_process import (LogitNormalization,
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from transformers.generation.logits_process import (
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LogitsProcessorList,
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LogitNormalization,
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TemperatureLogitsWarper)
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LogitsProcessorList,
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TemperatureLogitsWarper
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)
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class TailFreeLogitsWarper(LogitsWarper):
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class TailFreeLogitsWarper(LogitsWarper):
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@ -11,12 +11,19 @@ import gradio as gr
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import torch
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import torch
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import transformers
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import transformers
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from datasets import Dataset, load_dataset
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from datasets import Dataset, load_dataset
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from peft import (LoraConfig, get_peft_model, prepare_model_for_kbit_training,
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from peft import (
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set_peft_model_state_dict)
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LoraConfig,
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get_peft_model,
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prepare_model_for_kbit_training,
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set_peft_model_state_dict
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)
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from modules import shared, ui, utils
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from modules import shared, ui, utils
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from modules.evaluate import (calculate_perplexity, generate_markdown_table,
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from modules.evaluate import (
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save_past_evaluations)
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calculate_perplexity,
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generate_markdown_table,
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save_past_evaluations
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)
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from modules.logging_colors import logger
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from modules.logging_colors import logger
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# This mapping is from a very recent commit, not yet released.
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# This mapping is from a very recent commit, not yet released.
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@ -25,8 +32,9 @@ try:
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from peft.utils.other import \
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from peft.utils.other import \
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TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as \
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TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as \
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model_to_lora_modules
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model_to_lora_modules
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from transformers.models.auto.modeling_auto import \
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from transformers.models.auto.modeling_auto import (
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MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
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MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
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)
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MODEL_CLASSES = {v: k for k, v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES}
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MODEL_CLASSES = {v: k for k, v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES}
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except:
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except:
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standard_modules = ["q_proj", "v_proj"]
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standard_modules = ["q_proj", "v_proj"]
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@ -201,8 +209,9 @@ def clean_path(base_path: str, path: str):
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def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str):
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def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str):
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if shared.args.monkey_patch:
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if shared.args.monkey_patch:
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from monkeypatch.peft_tuners_lora_monkey_patch import \
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from monkeypatch.peft_tuners_lora_monkey_patch import (
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replace_peft_model_with_gptq_lora_model
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replace_peft_model_with_gptq_lora_model
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)
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replace_peft_model_with_gptq_lora_model()
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replace_peft_model_with_gptq_lora_model()
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global WANT_INTERRUPT
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global WANT_INTERRUPT
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17
server.py
17
server.py
@ -38,12 +38,17 @@ from modules.github import clone_or_pull_repository
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from modules.html_generator import chat_html_wrapper
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from modules.html_generator import chat_html_wrapper
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from modules.LoRA import add_lora_to_model
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from modules.LoRA import add_lora_to_model
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from modules.models import load_model, unload_model
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from modules.models import load_model, unload_model
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from modules.models_settings import (apply_model_settings_to_state,
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from modules.models_settings import (
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get_model_settings_from_yamls,
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apply_model_settings_to_state,
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save_model_settings,
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get_model_settings_from_yamls,
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update_model_parameters)
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save_model_settings,
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from modules.text_generation import (generate_reply_wrapper,
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update_model_parameters
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get_encoded_length, stop_everything_event)
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)
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from modules.text_generation import (
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generate_reply_wrapper,
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get_encoded_length,
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stop_everything_event
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)
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def load_model_wrapper(selected_model, loader, autoload=False):
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def load_model_wrapper(selected_model, loader, autoload=False):
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