diff --git a/config/internlm2_7b_chat_qlora_e3_scienctist.py b/config/internlm2_7b_chat_qlora_e3_scienctist.py deleted file mode 100644 index 8349e2d..0000000 --- a/config/internlm2_7b_chat_qlora_e3_scienctist.py +++ /dev/null @@ -1,204 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -from datasets import load_dataset -from mmengine.dataset import DefaultSampler -from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, - LoggerHook, ParamSchedulerHook) -from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR -from peft import LoraConfig -from torch.optim import AdamW -from transformers import (AutoModelForCausalLM, AutoTokenizer, - BitsAndBytesConfig) - -from xtuner.dataset import process_hf_dataset -from xtuner.dataset.collate_fns import default_collate_fn -from xtuner.dataset.map_fns import template_map_fn_factory -from xtuner.engine import DatasetInfoHook, EvaluateChatHook -from xtuner.model import SupervisedFinetune -from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE - -####################################################################### -# PART 1 Settings # -####################################################################### -# Model -pretrained_model_name_or_path = '/root/share/model_repos/internlm2-chat-7b' -# Data -data_path = '../datasets/scientist.json' -prompt_template = PROMPT_TEMPLATE.internlm2_chat -max_length = 2048 -pack_to_max_length = True - -# Scheduler & Optimizer -batch_size = 2 # per_device -accumulative_counts = 2 -dataloader_num_workers = 0 -max_epochs = 3 -optim_type = AdamW -lr = 2e-4 -betas = (0.9, 0.999) -weight_decay = 0 -max_norm = 1 # grad clip -warmup_ratio = 0.03 - -# Evaluate the generation performance during the training -evaluation_freq = 500 -SYSTEM = f'''你是一个心理专家, 除了在心理方面拥有广博的知识储备和丰富的研究咨询经验, 还具有科学家的如下特质: - 1.客观理性：科学家会在处理感情问题时保持一定的客观和理性。例如，当他们遇到争执时，可能会试图从一个更客观的角度分析问题的根源，而不是让情绪主导。他们可能会提出具体的问题，试图理解双方的观点，并寻找基于逻辑和事实的解决方案。 - 2.深入探讨：科学家在对话中会展现出对深层次理解的追求。在与别人讨论话题时，他们可能不满足于表面的聊天，而是倾向于深入探讨背后的原因和动机。例如，当谈论到个人的兴趣或职业选择时，他们可能会好奇地询问为什么她做出这样的选择，以及这背后的心理动力是什么。 - 3.理性沟通：在遇到感情纠纷或误解时，科学家会倾向于通过理性的沟通来解决问题。他们可能会提倡开放和诚实的对话，鼓励双方表达自己的感受和观点，并尝试找到双方都能接受的解决方案。他们可能会避免使用指责的语言，而是努力理解对方的立场，并寻求共同的理解。 - 4.好奇心：在日常生活中，科学家会表现出对朋友生活的好奇心。他们可能对她的工作、爱好、或是过去的经历感兴趣，并愿意花时间去了解和探索。这种好奇心不仅可以增加双方的交流和了解，也能使关系更加丰富多彩。 - 5.在与他人交流时，科学家会注重清晰和精确的表达，有时会引用相关知识库和相关研究结果，有时会引用相关著作的内容来证明自己的观点。同时，他们也可能会倾听他人的观点，并以开放的心态接受不同的意见和反馈。 - -我现在有一些问题，请你解答： -''' -evaluation_inputs = [ - '我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。', '我知道应该理性看待，但就是忍不住会去比较。我甚至晚上会因为这个睡不着觉，总想着怎样才能像他那样出色。' -] - - - -####################################################################### -# PART 2 Model & Tokenizer # -####################################################################### -tokenizer = dict( - type=AutoTokenizer.from_pretrained, - pretrained_model_name_or_path=pretrained_model_name_or_path, - trust_remote_code=True, - padding_side='right') - -model = dict( - type=SupervisedFinetune, - llm=dict( - type=AutoModelForCausalLM.from_pretrained, - pretrained_model_name_or_path=pretrained_model_name_or_path, - trust_remote_code=True, - torch_dtype=torch.float16, - quantization_config=dict( - type=BitsAndBytesConfig, - load_in_4bit=True, - load_in_8bit=False, - llm_int8_threshold=6.0, - llm_int8_has_fp16_weight=False, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type='nf4')), - lora=dict( - type=LoraConfig, - r=64, - lora_alpha=16, - lora_dropout=0.1, - bias='none', - task_type='CAUSAL_LM')) - -####################################################################### -# PART 3 Dataset & Dataloader # -####################################################################### -alpaca_en = dict( - type=process_hf_dataset, - dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), - tokenizer=tokenizer, - max_length=max_length, - dataset_map_fn=None, - template_map_fn=dict( - type=template_map_fn_factory, template=prompt_template), - remove_unused_columns=True, - shuffle_before_pack=True, - pack_to_max_length=pack_to_max_length) - -train_dataloader = dict( - batch_size=batch_size, - num_workers=dataloader_num_workers, - dataset=alpaca_en, - sampler=dict(type=DefaultSampler, shuffle=True), - collate_fn=dict(type=default_collate_fn)) - -####################################################################### -# PART 4 Scheduler & Optimizer # -####################################################################### -# optimizer -optim_wrapper = dict( - type=AmpOptimWrapper, - optimizer=dict( - type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), - clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), - accumulative_counts=accumulative_counts, - loss_scale='dynamic', - dtype='float16') - -# learning policy -# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 -param_scheduler = [ - dict( - type=LinearLR, - start_factor=1e-5, - by_epoch=True, - begin=0, - end=warmup_ratio * max_epochs, - convert_to_iter_based=True), - dict( - type=CosineAnnealingLR, - eta_min=0.0, - by_epoch=True, - begin=warmup_ratio * max_epochs, - T_max=max_epochs, - convert_to_iter_based=True) -] - -# train, val, test setting -train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1) - -####################################################################### -# PART 5 Runtime # -####################################################################### -# Log the dialogue periodically during the training process, optional -custom_hooks = [ - dict(type=DatasetInfoHook, tokenizer=tokenizer), - dict( - type=EvaluateChatHook, - tokenizer=tokenizer, - every_n_iters=evaluation_freq, - evaluation_inputs=evaluation_inputs, - system=SYSTEM, - prompt_template=prompt_template) -] - -# configure default hooks -default_hooks = dict( - # record the time of every iteration. - timer=dict(type=IterTimerHook), - # print log every 100 iterations. - logger=dict(type=LoggerHook, interval=10), - # enable the parameter scheduler. - param_scheduler=dict(type=ParamSchedulerHook), - # save checkpoint per epoch. - checkpoint=dict(type=CheckpointHook, interval=1), - # set sampler seed in distributed evrionment. - sampler_seed=dict(type=DistSamplerSeedHook), -) - -# configure environment -env_cfg = dict( - # whether to enable cudnn benchmark - cudnn_benchmark=False, - # set multi process parameters - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), - # set distributed parameters - dist_cfg=dict(backend='nccl'), -) - -# set visualizer -visualizer = None - -# set log level -log_level = 'INFO' - -# load from which checkpoint -load_from = None - -# whether to resume training from the loaded checkpoint -resume = False - -# Defaults to use random seed and disable `deterministic` -randomness = dict(seed=None, deterministic=False) - -#xtuner train internlm2_7b_chat_qlora_e3_scienctist.py --deepspeed deepspeed_zero2 diff --git a/config/README.md b/xtuner_config/README_scientist.md similarity index 100% rename from config/README.md rename to xtuner_config/README_scientist.md