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SYSTEM = 'xtuner.utils.SYSTEM_TEMPLATE.alpaca'
accumulative_counts = 1
alpaca_en = dict(
dataset=dict(
data_files=dict(
train='/root/finetune/data/assistant_Tuner_change.jsonl'),
path='json',
type='datasets.load_dataset'),
dataset_map_fn=None,
max_length=2048,
pack_to_max_length=True,
remove_unused_columns=True,
shuffle_before_pack=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/finetune/models/internlm2_5-7b-chat',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset',
use_varlen_attn=False)
alpaca_en_path = '/root/finetune/data/assistant_Tuner_change.jsonl'
batch_size = 1
betas = (
0.9,
0.999,
)
custom_hooks = [
dict(
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/finetune/models/internlm2_5-7b-chat',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.DatasetInfoHook'),
dict(
evaluation_inputs=[
'请介绍一下你自己',
'Please introduce yourself',
],
every_n_iters=500,
prompt_template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
system='xtuner.utils.SYSTEM_TEMPLATE.alpaca',
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/finetune/models/internlm2_5-7b-chat',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.EvaluateChatHook'),
]
dataloader_num_workers = 0
default_hooks = dict(
checkpoint=dict(
by_epoch=False,
interval=500,
max_keep_ckpts=2,
type='mmengine.hooks.CheckpointHook'),
logger=dict(
interval=10,
log_metric_by_epoch=False,
type='mmengine.hooks.LoggerHook'),
param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
timer=dict(type='mmengine.hooks.IterTimerHook'))
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
evaluation_freq = 500
evaluation_inputs = [
'请介绍一下你自己',
'Please introduce yourself',
]
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
lr = 0.0002
max_epochs = 3
max_length = 2048
max_norm = 1
model = dict(
llm=dict(
pretrained_model_name_or_path=
'/root/finetune/models/internlm2_5-7b-chat',
quantization_config=dict(
bnb_4bit_compute_dtype='torch.float16',
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
llm_int8_has_fp16_weight=False,
llm_int8_threshold=6.0,
load_in_4bit=True,
load_in_8bit=False,
type='transformers.BitsAndBytesConfig'),
torch_dtype='torch.float16',
trust_remote_code=True,
type='transformers.AutoModelForCausalLM.from_pretrained'),
lora=dict(
bias='none',
lora_alpha=16,
lora_dropout=0.1,
r=64,
task_type='CAUSAL_LM',
type='peft.LoraConfig'),
type='xtuner.model.SupervisedFinetune',
use_varlen_attn=False)
optim_type = 'torch.optim.AdamW'
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=0.0002,
type='torch.optim.AdamW',
weight_decay=0),
type='DeepSpeedOptimWrapper')
pack_to_max_length = True
param_scheduler = [
dict(
begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=0.09,
start_factor=1e-05,
type='mmengine.optim.LinearLR'),
dict(
begin=0.09,
by_epoch=True,
convert_to_iter_based=True,
end=3,
eta_min=0.0,
type='mmengine.optim.CosineAnnealingLR'),
]
pretrained_model_name_or_path = '/root/finetune/models/internlm2_5-7b-chat'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm2_chat'
randomness = dict(deterministic=False, seed=None)
resume = False
runner_type = 'FlexibleRunner'
sampler = 'mmengine.dataset.DefaultSampler'
save_steps = 500
save_total_limit = 2
sequence_parallel_size = 1
strategy = dict(
config=dict(
bf16=dict(enabled=True),
fp16=dict(enabled=False, initial_scale_power=16),
gradient_accumulation_steps='auto',
gradient_clipping='auto',
train_micro_batch_size_per_gpu='auto',
zero_allow_untested_optimizer=True,
zero_force_ds_cpu_optimizer=False,
zero_optimization=dict(overlap_comm=True, stage=2)),
exclude_frozen_parameters=True,
gradient_accumulation_steps=1,
gradient_clipping=1,
sequence_parallel_size=1,
train_micro_batch_size_per_gpu=1,
type='xtuner.engine.DeepSpeedStrategy')
tokenizer = dict(
padding_side='right',
pretrained_model_name_or_path='/root/finetune/models/internlm2_5-7b-chat',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained')
train_cfg = dict(max_epochs=3, type='xtuner.engine.runner.TrainLoop')
train_dataloader = dict(
batch_size=1,
collate_fn=dict(
type='xtuner.dataset.collate_fns.default_collate_fn',
use_varlen_attn=False),
dataset=dict(
dataset=dict(
data_files=dict(
train='/root/finetune/data/assistant_Tuner_change.jsonl'),
path='json',
type='datasets.load_dataset'),
dataset_map_fn=None,
max_length=2048,
pack_to_max_length=True,
remove_unused_columns=True,
shuffle_before_pack=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path=
'/root/finetune/models/internlm2_5-7b-chat',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.process_hf_dataset',
use_varlen_attn=False),
num_workers=0,
sampler=dict(shuffle=True, type='mmengine.dataset.DefaultSampler'))
use_varlen_attn = False
visualizer = None
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs/assistTuner'
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