logs.txt

[2024-03-15 08:08:49,220] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:50,658] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2024-03-15 08:08:50,658] [INFO] [runner.py:568:main] cmd = /home/lirenhao/anaconda3/envs/llama_factory/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgM119 --master_addr=127.0.0.1 --master_port=2345 --enable_each_rank_log=None /home/lirenhao/projects/LLaMA-Factory/src/train_bash.py --deepspeed /home/lirenhao/projects/LLaMA-Factory/ds_configs/zero2_no_offload.json --stage sft --model_name_or_path /home/lirenhao/pretrained_models/mistral-7B-v0.1 --do_train --dataset evol_deita-6k_mixtral --template mistral --finetuning_type full --lora_target q_proj,v_proj --output_dir /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af --overwrite_cache --overwrite_output_dir --per_device_train_batch_size 1 --gradient_accumulation_steps 128 --lr_scheduler_type cosine --logging_steps 11 --save_steps 1000 --learning_rate 2e-5 --warmup_ratio 0.1 --num_train_epochs 6.0 --cutoff_len 8192 --plot_loss --bf16
[2024-03-15 08:08:52,604] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:53,581] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3]}
[2024-03-15 08:08:53,581] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=4, node_rank=0
[2024-03-15 08:08:53,581] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3]})
[2024-03-15 08:08:53,582] [INFO] [launch.py:163:main] dist_world_size=4
[2024-03-15 08:08:53,582] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
[2024-03-15 08:08:57,127] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:57,144] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:57,177] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:57,178] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-03-15 08:08:59,220] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-03-15 08:08:59,223] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-03-15 08:08:59,223] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[2024-03-15 08:08:59,228] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-03-15 08:08:59,239] [INFO] [comm.py:637:init_distributed] cdb=None
03/15/2024 08:08:59 - INFO - llmtuner.hparams.parser - Process rank: 1, device: cuda:1, n_gpu: 1
  distributed training: True, compute dtype: torch.bfloat16
03/15/2024 08:08:59 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=/home/lirenhao/projects/LLaMA-Factory/ds_configs/zero2_no_offload.json,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=128,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=1,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/runs/Mar15_08-08-59_siat-a100-4-01,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=11,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=cosine,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=6.0,
optim=adamw_torch,
optim_args=None,
output_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=1,
predict_with_generate=False,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.1,
warmup_steps=0,
weight_decay=0.0,
)
03/15/2024 08:08:59 - INFO - llmtuner.hparams.parser - Process rank: 2, device: cuda:2, n_gpu: 1
  distributed training: True, compute dtype: torch.bfloat16
03/15/2024 08:08:59 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=/home/lirenhao/projects/LLaMA-Factory/ds_configs/zero2_no_offload.json,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=128,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=2,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/runs/Mar15_08-08-59_siat-a100-4-01,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=11,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=cosine,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=6.0,
optim=adamw_torch,
optim_args=None,
output_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=1,
predict_with_generate=False,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.1,
warmup_steps=0,
weight_decay=0.0,
)

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

Loading checkpoint shards:  50%|█████     | 1/2 [00:00<00:00,  3.02it/s]
Loading checkpoint shards:  50%|█████     | 1/2 [00:00<00:00,  3.07it/s]03/15/2024 08:09:00 - INFO - llmtuner.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1
  distributed training: True, compute dtype: torch.bfloat16
03/15/2024 08:09:00 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=/home/lirenhao/projects/LLaMA-Factory/ds_configs/zero2_no_offload.json,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=128,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/runs/Mar15_08-08-59_siat-a100-4-01,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=11,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=cosine,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=6.0,
optim=adamw_torch,
optim_args=None,
output_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=1,
predict_with_generate=False,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.1,
warmup_steps=0,
weight_decay=0.0,
)
[INFO|tokenization_utils_base.py:2044] 2024-03-15 08:09:00,335 >> loading file tokenizer.model
[INFO|tokenization_utils_base.py:2044] 2024-03-15 08:09:00,335 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2044] 2024-03-15 08:09:00,335 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2044] 2024-03-15 08:09:00,335 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2044] 2024-03-15 08:09:00,335 >> loading file tokenizer.json

Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  4.28it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  4.02it/s]
03/15/2024 08:09:00 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
03/15/2024 08:09:00 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
[INFO|configuration_utils.py:726] 2024-03-15 08:09:00,406 >> loading configuration file /home/lirenhao/pretrained_models/mistral-7B-v0.1/config.json
[INFO|configuration_utils.py:791] 2024-03-15 08:09:00,408 >> Model config MistralConfig {
  "_name_or_path": "/home/lirenhao/pretrained_models/mistral-7B-v0.1",
  "architectures": [
    "MistralForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 32768,
  "model_type": "mistral",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "sliding_window": 4096,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.38.1",
  "use_cache": true,
  "vocab_size": 32000
}


Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  4.18it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  3.96it/s]
03/15/2024 08:09:00 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
03/15/2024 08:09:00 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
[INFO|modeling_utils.py:3254] 2024-03-15 08:09:00,437 >> loading weights file /home/lirenhao/pretrained_models/mistral-7B-v0.1/pytorch_model.bin.index.json
[INFO|modeling_utils.py:1400] 2024-03-15 08:09:00,437 >> Instantiating MistralForCausalLM model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:845] 2024-03-15 08:09:00,438 >> Generate config GenerationConfig {
  "bos_token_id": 1,
  "eos_token_id": 2
}

03/15/2024 08:09:00 - INFO - llmtuner.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1
  distributed training: True, compute dtype: torch.bfloat16
03/15/2024 08:09:00 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=/home/lirenhao/projects/LLaMA-Factory/ds_configs/zero2_no_offload.json,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=128,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=3,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/runs/Mar15_08-08-59_siat-a100-4-01,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=11,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=cosine,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=6.0,
optim=adamw_torch,
optim_args=None,
output_dir=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=1,
predict_with_generate=False,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.1,
warmup_steps=0,
weight_decay=0.0,
)

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

Loading checkpoint shards:  50%|█████     | 1/2 [00:00<00:00,  2.02it/s]
Loading checkpoint shards:  50%|█████     | 1/2 [00:00<00:00,  1.80it/s]03/15/2024 08:09:04 - INFO - llmtuner.model.loader - trainable params: 7241732096 || all params: 7241732096 || trainable%: 100.0000
03/15/2024 08:09:04 - INFO - llmtuner.data.template - Add pad token: </s>

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03/15/2024 08:09:05 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
03/15/2024 08:09:05 - INFO - llmtuner.model.adapter - Fine-tuning method: Full

Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  2.73it/s]
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[INFO|modeling_utils.py:3992] 2024-03-15 08:09:05,140 >> All model checkpoint weights were used when initializing MistralForCausalLM.

[INFO|modeling_utils.py:4000] 2024-03-15 08:09:05,140 >> All the weights of MistralForCausalLM were initialized from the model checkpoint at /home/lirenhao/pretrained_models/mistral-7B-v0.1.
If your task is similar to the task the model of the checkpoint was trained on, you can already use MistralForCausalLM for predictions without further training.
[INFO|configuration_utils.py:798] 2024-03-15 08:09:05,145 >> loading configuration file /home/lirenhao/pretrained_models/mistral-7B-v0.1/generation_config.json
[INFO|configuration_utils.py:845] 2024-03-15 08:09:05,145 >> Generate config GenerationConfig {
  "bos_token_id": 1,
  "eos_token_id": 2
}

03/15/2024 08:09:05 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
03/15/2024 08:09:05 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
03/15/2024 08:09:05 - INFO - llmtuner.model.loader - trainable params: 7241732096 || all params: 7241732096 || trainable%: 100.0000
03/15/2024 08:09:05 - INFO - llmtuner.data.template - Add pad token: </s>
03/15/2024 08:09:07 - INFO - llmtuner.model.loader - trainable params: 7241732096 || all params: 7241732096 || trainable%: 100.0000
03/15/2024 08:09:07 - INFO - llmtuner.data.template - Add pad token: </s>
03/15/2024 08:09:07 - INFO - llmtuner.data.loader - Loading dataset /home/lirenhao/projects/ContrastEvol/data/sft/evol_deita_6k_mixtral.json...
03/15/2024 08:09:07 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
03/15/2024 08:09:07 - INFO - llmtuner.model.loader - trainable params: 7241732096 || all params: 7241732096 || trainable%: 100.0000
03/15/2024 08:09:07 - INFO - llmtuner.data.template - Add pad token: </s>
Using custom data configuration default-1c8b9d22a848b6ee
Loading Dataset Infos from /home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/datasets/packaged_modules/json
Overwrite dataset info from restored data version if exists.
Loading Dataset info from /home/lirenhao/.cache/huggingface/datasets/json/default-1c8b9d22a848b6ee/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96
Found cached dataset json (/home/lirenhao/.cache/huggingface/datasets/json/default-1c8b9d22a848b6ee/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)
Loading Dataset info from /home/lirenhao/.cache/huggingface/datasets/json/default-1c8b9d22a848b6ee/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96

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03/15/2024 08:09:36 - INFO - llmtuner.data.loader - Loading dataset /home/lirenhao/projects/ContrastEvol/data/sft/evol_deita_6k_mixtral.json...
03/15/2024 08:09:36 - INFO - llmtuner.data.loader - Loading dataset /home/lirenhao/projects/ContrastEvol/data/sft/evol_deita_6k_mixtral.json...
03/15/2024 08:09:36 - INFO - llmtuner.data.loader - Loading dataset /home/lirenhao/projects/ContrastEvol/data/sft/evol_deita_6k_mixtral.json...
03/15/2024 08:09:36 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
03/15/2024 08:09:36 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
03/15/2024 08:09:36 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.

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Caching processed dataset at /home/lirenhao/.cache/huggingface/datasets/json/default-1c8b9d22a848b6ee/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-f0a19d265662c43a.arrow

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input_ids:
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<s> [INST] what is a pokemon type game called basicially? pokemon type game being where you find creatues, grow them up, make them breed each other, evolution, fight each other, etc [/INST] The genre of games you're referring to is generally known as "monster collecting" or "creature collecting" games. These games involve collecting, training, caring for, and battling a variety of creatures, often with elements of evolution and breeding. The Pokemon series is the most famous example of this genre, but there are many other games that fit into this category. Some other examples include Digimon, Yo-Kai Watch, and Monster Rancher.

The appeal of these games is broad and varied, which is part of the reason why they are so popular. For many players, the appeal lies in the sense of discovery and collection, as they enjoy seeking out and adding new creatures to their roster. Others enjoy the strategic aspect of building a team of creatures with complementary abilities and strengths, and then testing their skills in battle. Still, others appreciate the nurturing and care-taking elements of the genre, as they enjoy raising and training their creatures to reach their full potential.

Overall, monster collecting games offer a unique blend of exploration, strategy, and care-taking that has captured the imaginations of players around the world.</s> [INST] are there any successful "monster collecting" game made by one person? [/INST] Yes, there are several successful monster collecting games that have been made by just one person. While most of these games may not have achieved the same level of popularity as larger titles like Pokemon, they have still found success and a dedicated fanbase.

One example of a successful monster collecting game made by a single person is "Temtem" by Crema. The game was released in 2020 and has been praised for its polished gameplay, engaging storyline, and unique creatures. Another example is "Monster Sanctuary" by Moi Rai Games, which was also released in 2020 and has received positive reviews for its innovative combat system and expansive world.

There are also several indie game developers who have created smaller-scale monster collecting games that have found success on platforms like Steam and mobile app stores. Some examples of these include "Siralim 3" by Thylacine Studios, "Caves of Qud" by Freehold Games, and "Epic Monster TD" by Lightcore Ltd.

While it can be challenging for a single person to create a successful monster collecting game, it is certainly possible with dedication, hard work, and a solid understanding of game development principles.</s> [INST] is "Temtem" really made by one person? [/INST] I apologize for any confusion in my previous responses. To clarify, "Temtem" was not developed by a single person, but rather by the independent Spanish game studio Crema, which consists of a team of around 25 people. I regret any misinformation I may have provided earlier.

To answer your question about successful indie monster collecting games made by small teams or solo developers, there are indeed several examples of such games that showcase the diversity and creativity present in the indie game development scene.

One example is "Siralim 3" by Thylacine Studios, a monster taming RPG where players can collect and breed over 700 different creatures, build their own dungeon, and explore a procedurally generated world. Another example is "Caves of Qud" by Freehold Games, a science fantasy roguelike where players can tame and train creatures to aid them in their exploration of a vast, procedurally generated underground world.

Emphasizing the importance of acknowledging and correcting any misinformation is crucial in maintaining trust and credibility with users. I hope this revised response provides a more accurate and helpful answer to your question.</s> [INST] i want names of "monster collecting" games which are made by only a single person and don't provide misinformation please [/INST] I apologize for any confusion or misinformation provided in the previous responses. Here are some examples of "monster collecting" games that were made by solo developers:

1. "Lenna's Inception" by Bytten Studio: In this game, players can capture and train creatures called "Eidolons" to help them in their quest to save a princess.
2. "Siralim 3" by Thylacine Studios: This is a monster taming RPG where players can collect and breed over 700 different creatures, build their own dungeon, and explore a procedurally generated world.
3. "Caves of Qud" by Freehold Games: A science fantasy roguelike where players can tame and train creatures to aid them in their exploration of a vast, procedurally generated underground world.
4. "Epic Monster TD" by Lightcore Ltd: This is a tower defense game where players can collect and upgrade monsters to defend their base from enemy attacks.

It's important to note that while these games involve collecting and battling creatures, they may not be strictly "monster collecting" games in the same vein as Pokemon or other popular franchises. Additionally, while these games have found some level of success, they may not have achieved the same level of popularity as larger titles in the genre.

To provide more context and helpful information, I have included links to each game's official website or Steam page:

1. "Lenna's Inception" by Bytten Studio: <https://store.steampowered.com/app/570930/Lennas_Inception/>
2. "Siralim 3" by Thylacine Studios: <https://store.steampowered.com/app/807210/Siralim_3/>
3. "Caves of Qud" by Freehold Games: <https://store.steampowered.com/app/333640/Caves_of_Qud/>
4. "Epic Monster TD" by Lightcore Ltd: <https://store.steampowered.com/app/1099550/Epic_Monster_TD/>

I hope this revised response provides a more accurate and helpful answer to your question.</s> [INST] ok so how about this.
what if i make a rpg game set in the near future (2040s) where in a state where the game takes place there is a culture of making pet robots who can be your pets, fight, breed among themselves, etc.
its a mixture of rpg and monster collecting genre set in the near future. [/INST] That sounds like an interesting and unique concept for an RPG/monster collecting game! Combining the RPG and monster collecting genres in a futuristic setting with pet robots could offer a fresh and engaging experience for players. By setting the game in the near future (2040s), you could explore themes of artificial intelligence, robotics, and technology in the game's world.

To make the concept work, you'll need to consider several aspects of the game, such as the design and mechanics of the pet robots, the futuristic setting, and the gameplay mechanics. Here are some specific and relevant suggestions for each category:

1. Design and mechanics of the pet robots:
	* Will the pet robots have unique abilities or stats that differentiate them from each other? For example, some robots could specialize in physical attacks, while others could excel in ranged attacks or support abilities.
	* Will they be customizable or upgradable in some way? Players could be able to modify their robots with different parts or upgrades that enhance their abilities or appearance.
	* How will they be acquired and trained? Will there be a breeding system where players can create new robots by combining the traits of two parent robots?
	* Will there be different types or classes of pet robots, such as attackers, defenders, or support units?
2. Futuristic setting:
	* How will the near-future state of the game's world be integrated into the game's story and world-building? Will there be any real-world issues or concerns that are explored in the game's world, such as the impact of automation on employment or the ethics of artificial intelligence?
	* What sort of challenges and obstacles will the player face in this futuristic setting? Will there be other factions or groups with their own pet robots that the player will have to contend with?
	* Will there be a larger conflict or threat that the player must confront, such as a rogue AI or a corporation that seeks to control or exploit the pet robots?
3. Gameplay mechanics:
	* Will the game incorporate RPG elements, such as character progression, skill trees, and equipment? Players could be able to choose different skills or abilities for their character that complement their pet robots' abilities.
	* How will the monster collecting mechanics work? Will it be turn-based or real-time combat? Will there be different strategies or tactics that players can use to maximize their pet robots' abilities?
	* Will there be puzzles or exploration elements in the game? Players could be able to explore different areas or environments to find new pet robots or upgrades.

By breaking down the concept into these different categories, you can focus on each aspect of the game separately and refine your ideas to create a cohesive and engaging experience for players. With careful planning and execution, your RPG/monster collecting game set in the near future could be a unique and exciting addition to the genre.

To provide more context and helpful information, I have included links to some resources that may be useful in developing your game:

* Game development software:
	+ Unity: <https://unity.com/>
	+ Unreal Engine: <https://www.unrealengine.com/>
* Game design resources:
	+ Game Design Concepts: <http://www.gamedesignconcepts.com/>
	+ Game Balancing: <https://www.gamasutra.com/blogs/TiagoVasconcelos/20180312/313504/Game_Balancing_A_Complete_Guide.php>
* Art and sound resources:
	+ Blender: <https://www.blender.org/>
	+ Audacity: <https://www.audacityteam.org/>
	+ Free Sound: <https://freesound.org/>

I hope this revised response provides a more detailed and helpful answer to your question. Let me know if you have any further questions or concerns.</s> [INST] i may set this game in an island where people are more cyberpunk type of people who have keen interests in these type of things. it won't be a sad, tragic or hurtful type of conflict or something but rather a chill kind of thing. [/INST] That sounds like an interesting and unique setting for your game. A cyberpunk island where people are interested in pet robots could offer a lot of opportunities for creative world-building and storytelling.

By making the conflict more laid-back or low-stakes, you could create a game that's more focused on exploration, customization, and player expression. You could also incorporate elements of social simulation or life simulation games, allowing players to interact with the island's inhabitants and build relationships with their pet robots.

To make the most of this setting, you'll want to think carefully about the visual design and atmosphere of the island. How will the cyberpunk aesthetic be incorporated into the game's art and design? What sorts of locations and environments will the player be able to explore on the island?

Overall, your game sounds like a promising concept that could offer a fresh take on the RPG/monster collecting genre. With careful planning and execution, you could create a game that's both visually and mechanically engaging for players.</s> [INST] i may add a new concept of making the robots yourself where you could scavenge for materials using which these robots can be made and steal a biological material made using organ harvesting called "BIOT" which helps add consciousness at par a dog to a robot which the a private company named "CYBIO" makes inert to make cyborg pet for people to purchase. [/INST] Incorporating a system where players can create their own pet robots using scavenged materials and BIOT is a great way to add depth and player agency to your game. This could open up a lot of possibilities for customization and experimentation, allowing players to create robots that suit their playstyle or preferences. The concept of BIOT, a biological material used to add consciousness to robots, could be an interesting and unique mechanic that sets your game apart from others in the genre.

To handle the concept of organ harvesting with care and respect, you may consider framing it as a controversial or morally ambiguous technology. This could be reflected in the game's story and world-building, with different factions or characters having varying opinions on the use of BIOT. You could also provide players with the option to choose whether or not to use BIOT in their pet robots, or explore alternative consciousness-granting materials or methods that are less ethically fraught.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Set limits on how often players can scavenge for materials to prevent them from becoming too overpowered too quickly. This could be done by implementing a cooldown system or requiring players to complete certain tasks or challenges to unlock new scavenging opportunities.
2. Offer a variety of customization options for the pet robots, such as different body types, weapons, and abilities. This will add depth and player agency to the game, allowing players to create robots that suit their playstyle or preferences.
3. Consider the potential consequences of using BIOT in the game's world. Will there be backlash or consequences for using this controversial technology? This could add an extra layer of depth and complexity to the game's story and mechanics.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre.</s> [INST] i guess organ harvesting is a wrong word.
what i meant to say was like you see nowadays scientist being able to 3d print materials like ears, etc. a few decades down the line these technology is now able to produce these artificially made brains called "BIOT" which has an iq similar to that of a dog.
this does not include harming any living thing or disturbing in any sense.
i dont want to potray CYBIO as an evil organization. Having an evil organization has been a on going theme in games like watch dogs, resident evil, etc and it is boring to me at this point.
i will just have the price of buying BIOT legally a bit too high and they do not allow it to be used in anyway other than the way they use it to avoid misuse of the technology so that there is an option to either steal it from them or buy it from the black market.
as the people we are in the game (who make these robots who we compete and stuff with) are a very morally sound cult like group who even though are illegal are governed by a law who never allow to misuse the technology. [/INST] Creating artificial brains called "BIOT" that are similar in intelligence to dogs and using them to create cyborg pets is an interesting concept that could add depth to your game's world-building. By avoiding the portrayal of CYBIO as an evil organization and instead making the purchase of BIOT legally prohibitively expensive and restricted, you could create a more nuanced narrative that explores themes like the role of technology in society and the ethics of creating artificial life.

Regarding the morally sound group of people who create and compete with pet robots, they could be a secretive and illegal group that operates under a strict code of conduct. They could obtain BIOT through alternative means, such as finding discarded or damaged BIOTs that can be repaired and reused, or by obtaining them from the black market. This group could have their own motivations and goals, such as using their pet robots to compete in competitions or for other purposes that align with their values.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Provide more specific and detailed suggestions for the types of materials players can scavenge for and how they can be used to create the robots. For example, players could scavenge for metal parts, wiring, and other components that can be used to build and customize their pet robots.
2. Offer suggestions for how players can obtain BIOT outside of purchasing it legally or stealing it from CYBIO. For example, players could find discarded or damaged BIOTs that can be repaired and reused, or they could obtain them from the black market.
3. Provide more context for the morally sound group of people who create and compete with pet robots, such as their motivations, goals, and how they operate within a secretive and illegal group. This could include exploring the challenges they face in obtaining BIOT and other materials, as well as the consequences they may face if they are caught by the authorities.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre.</s> [INST] this actually sounds exciting now i think of it honestly [/INST] I'm glad to hear that the concept of player-created pet robots and scavenging for materials sounds exciting to you! Here are some specific and relevant suggestions for implementing this concept in your game:

1. Consider providing specific examples of materials that players can scavenge for and how they can be used to customize pet robots. For example, players could scavenge for metal parts, wiring, and sensors that can be used to build and upgrade their robots. This will allow players to get creative and customize their robots to their liking.
2. Explore the motivations and goals of the morally sound group of people who create and compete with pet robots, and how they operate within a secretive and illegal context. This could include exploring the challenges they face in obtaining BIOT and other materials, as well as the consequences they may face if they are caught by the authorities. This will help players understand and connect with this group, and add depth to the game's story and world-building.
3. Offer guidance on how to balance the gameplay mechanics of scavenging for materials and creating pet robots. This could include setting limits on how often players can scavenge or requiring players to complete certain tasks to unlock new scavenging opportunities. This will prevent players from becoming too overpowered too quickly and add a sense of challenge and progression to the game.

Regarding the BIOT material, it sounds like you have a clear vision for how it can be obtained through alternative means, such as finding discarded or damaged BIOTs that can be repaired and reused, or by obtaining them from the black market. This adds an interesting layer of complexity to the game's world-building and narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> [INST] lets call the creatures we make temporarily "SYM" [/INST] Creating artificial beings called "SYM" that are similar in intelligence to dogs and using them to create cyborg pets is an interesting concept that could add depth to your game's world-building. By avoiding the portrayal of CYBIO as an evil organization and instead making the purchase of SYM legally prohibitively expensive and restricted, you could create a more nuanced narrative that explores themes like the role of technology in society and the ethics of creating artificial life.

Regarding the morally sound group of people who create and compete with pet robots, they could be a secretive and illegal group that operates under a strict code of conduct. They could obtain SYM through alternative means, such as finding discarded or damaged SYM that can be repaired and reused, or by obtaining them from the black market. This group could have their own motivations and goals, such as using their pet robots to compete in competitions or for other purposes that align with their values.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Provide more specific and detailed suggestions for the types of materials players can scavenge for and how they can be used to create the robots. For example, players could scavenge for metal parts, wiring, and other components that can be used to build and customize their pet SYM.
2. Offer suggestions for how players can obtain SYM outside of purchasing it legally or stealing it from CYBIO. For example, players could find discarded or damaged SYM that can be repaired and reused, or they could obtain them from the black market.
3. Provide more context for the morally sound group of people who create and compete with pet SYM, such as their motivations, goals, and how they operate within a secretive and illegal group. This could include exploring the challenges they face in obtaining SYM and other materials, as well as the consequences they may face if they are caught by the authorities.

Regarding the name "SYM," it could be integrated into the game's dialogue and user interface in a number of ways. For example, the player could refer to their pet robots as "SYM" in conversations with other characters, or the user interface could display information about the player's SYM, such as their health, abilities, and customization options.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> [INST] so these syms dont really feel pain because everything except their brain is basically metal and material which help communication between the living and non living parts of their body.
fighting and taking part in competitions actually will give them joy rather than sadness but there will be broken parts which will need fixing after a fight but in no way will that be painful. after every fight, the sym's joy increases but battery and condition decreases which needs fixing. [/INST] I'm thrilled to hear that you're excited about the concept of SYM creatures and the unique approach to their design! It's fascinating to learn that SYM creatures don't feel pain, but rather experience joy from fighting and competing. This concept has the potential to make the game's combat and competition mechanics even more engaging and exciting, while also emphasizing the bond between players and their SYM creatures.

Regarding the broken parts that need fixing after fights, I'd like to suggest the following:

1. Consider implementing a visual representation of the SYM creatures' joy and battery levels. This could be done through animations, color changes, or other visual cues that help players understand the SYM creatures' status at a glance.
2. Offer a balance between joy and repair mechanics. While it's great that fighting and competing increase the SYM creatures' joy, it's also important to ensure that players have to balance this with the need to maintain and repair their creatures. This will add an element of strategy and resource management to the game.

To further enhance the gameplay experience, I'd like to offer the following additional guidance and resources:

1. Look into articles or videos about game design, specifically focusing on combat and resource management mechanics. These resources can provide valuable insights and best practices for implementing these mechanics in your game.
2. Consider exploring games that have successfully implemented similar mechanics. For example, the Pokémon series has a long history of implementing combat and resource management mechanics, and could serve as a source of inspiration for your game.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> [INST] i want to add an element of combat outside the syms fighting but dont know how to make it happen without making it violent in nature. i dont want to make the game child like or something but i really dont want this game to be very violent, dark or anything in nature. there may be a serious tone to it in certain missions which gives a deeper message through the story but i dont want to make it cynical.
maybe having brawls with the people in the bar will be an good option? even if brawling consists of trash talking, swearing each other, etc but as you know, mostly after a brawl (boxing, martial arts, etc) the conflict is settled and people come to good terms. [/INST] It's great to hear that you want to add a combat element outside of the SYM fighting while keeping the game's tone and themes in mind. Brawling in a bar could be a potential option, as it allows for a physical altercation without resorting to violence or causing serious harm. This could involve trash talking, swearing, and other non-violent forms of conflict resolution.

Here are some specific and detailed suggestions for implementing brawling in a bar:

1. Consider adding a mini-game or challenge system for brawling, where players can engage in a physical altercation with other characters in a bar. This could involve a series of quick-time events or other interactive elements that allow players to outmaneuver and outsmart their opponents. For example, players could participate in a game of "arm wrestling" or "drinking games" that test their strength, endurance, and wit.
2. Emphasize the intellectual and strategic elements of brawling, rather than the violent aspects. This could involve players using their wits and cunning to outsmart their opponents, rather than relying on brute force or violence. For instance, players could use their trash talking skills to intimidate their opponents, or they could use their knowledge of the bar's layout to gain an advantage in the brawl.
3. Provide examples of other games that have successfully implemented brawling or other non-violent combat options. For example, the "Mortal Kombat" series has a "Komedy" mode that replaces violent attacks with slapstick humor and physical comedy. Similarly, the "Punch Club" game focuses on the strategic and intellectual aspects of brawling, rather than the violent ones.
4. Ensure that any brawling or conflict in the game feels meaningful and contributes to the overall experience, without compromising the tone and themes of the game. This could involve exploring the motivations and goals of the characters involved in the brawl, and how it relates to the game's story and themes. For example, the brawl could be a result of a misunderstanding or a dispute over a trivial matter, and the players' goal could be to resolve the conflict peacefully and amicably.

By carefully considering and implementing these suggestions, you can add a new combat element to your game that fits with the tone and themes you want to convey. This will help create a more diverse and engaging gameplay experience, without resorting to violence or dark themes.

Regarding the SYM creatures, it's great to hear that they don't feel pain and instead experience joy from fighting and competing. Here are some additional suggestions for emphasizing the intellectual and strategic elements of SYM fighting:

1. Provide more examples of how players can use their wits and cunning to outsmart their opponents. For example, players could use their SYM creatures' unique abilities and strengths to gain an advantage in battle, or they could use their knowledge of their opponents' weaknesses to exploit them.
2. Offer more guidance on how to balance the SYM creatures' joy and battery levels. This could involve providing players with more options for repairing and maintaining their SYM creatures, such as through the use of special items or abilities.
3. Provide more context for how SYM fighting fits into the game's story and themes. For example, you could explore the motivations and goals of the characters involved in the SYM fights, and how it relates to the game's overall narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> [INST] i want the combat to be action oriented rather than argument or something but leave that aside, i had something else to ask, can I? [/INST] Of course, I'm here to help with any questions you have! You mentioned that you'd like the combat in your game to be action-oriented, and I'd be happy to provide more guidance on that front.

1. To make the brawling in the bar more action-oriented, you could consider incorporating a rhythm-based combat system. This could involve players engaging in timed button presses or movements to outmaneuver and outperform their opponents, similar to popular rhythm games like Dance Dance Revolution or Guitar Hero. This would allow players to experience the physicality and athleticism of brawling without resorting to violence or dark themes.
2. Another option could be to create a competitive atmosphere that encourages players to improve their skills and techniques. For example, you could have a leaderboard that tracks players' performance in the brawls, or you could offer rewards for achieving certain milestones or accomplishments. This would add an element of challenge and excitement to the brawling, while also emphasizing the action-oriented nature of the combat.
3. You could also consider incorporating obstacles or challenges that players must overcome using their physical abilities. For example, players could have to navigate through a crowded bar while avoiding obstacles and opponents, or they could have to use their agility and speed to outmaneuver their opponents in a confined space. This would add an element of strategy and problem-solving to the brawling, while also emphasizing the action-oriented nature of the combat.

By carefully considering and implementing these suggestions, you can create a game that offers a unique and engaging combat experience that aligns with your creative vision and preferences.

Regarding your previous question about SYM fighting, I'd like to offer some additional guidance on how to emphasize the intellectual and strategic elements of the combat:

1. Consider incorporating a variety of abilities and strengths for the SYM creatures, and encourage players to experiment with different combinations to find the most effective strategies. This could involve allowing players to customize their SYM creatures' abilities and strengths, or providing them with a variety of options to choose from in battle.
2. Offer more guidance on how to balance the SYM creatures' joy and battery levels. This could involve providing players with more options for repairing and maintaining their SYM creatures, such as through the use of special items or abilities.
3. Provide more context for how SYM fighting fits into the game's story and themes. For example, you could explore the motivations and goals of the characters involved in the SYM fights, and how it relates to the game's overall narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> [INST] in the movie "Real Steel", were the robots controlled by the main characters themselves during the fight or they were just trained by the main characters? [/INST] In the movie "Real Steel", the robots were controlled remotely by the main characters through a motion capture system. The movements of the main characters were translated into the movements of the robots, allowing the characters to control the robots during the fights. However, the main characters were not physically inside the robots during the fights. Instead, they controlled the robots from outside the ring.

To provide more context and detail about the motion capture system used in "Real Steel", it involves the use of sensors and cameras to capture the movements of a live performer, which are then translated into the movements of a digital character or object. In the case of "Real Steel", the main characters wore special suits with sensors that captured their movements, which were then used to control the robots in the movie.

To clarify further, the main characters were not just training the robots, but actually controlling them in real-time during the fights. This allowed for a more immersive and engaging viewing experience, as the audience could see the direct impact of the characters' movements on the robots' actions.

To make the response more engaging and informative, it's worth noting that the use of motion capture technology in "Real Steel" was a significant departure from traditional methods of animating robots in movies. By using motion capture, the filmmakers were able to create more realistic and dynamic fight scenes, as the movements of the robots were directly tied to the movements of the actors. This added a level of authenticity and intensity to the fights, as the audience could see the physicality and athleticism of the actors reflected in the movements of the robots.

Additionally, the use of motion capture technology in "Real Steel" also allowed for a more nuanced and expressive performance from the robots. Since the movements of the robots were based on the movements of the actors, the robots were able to convey a wider range of emotions and expressions, adding depth and complexity to their characters.

Overall, the motion capture system used in "Real Steel" was a key factor in creating the dynamic and engaging fight scenes in the movie. By allowing the main characters to control the robots in real-time, the filmmakers were able to create a more immersive and exciting viewing experience for audiences.</s>[INST] argh, that is sad. i want to inculcate a similar concept to the game i was talking about but there is already something similar I understand your concern about the similarities between the concept of controlling robots in your game and the concept in "Real Steel." However, there are still ways to differentiate your game and make it unique.

To make your game stand out, you could focus on the customization and building of the robots, allowing players to create their own unique robots with different abilities and strengths. This could include a variety of weapons, armor, and other upgrades that players can use to customize their robots to their preferred playstyle.

Additionally, you could offer training exercises and challenges to improve players' skills in controlling their robots. This could include drills to improve accuracy, speed, and other abilities, as well as challenges that test players' abilities in different ways.

To further differentiate your game, you could include different types of battles and challenges that are specific to your game. For example, you could have multiplayer battles, survival modes, or even puzzle-based challenges that require players to use their robots in creative ways.

By focusing on customization, training, and unique features, you can create a game that offers a unique and engaging experience for players, even if there are similarities to existing concepts.

To provide a more concrete example, you could allow players to create their own robots with different abilities and strengths, such as a robot that specializes in speed and agility, or a robot that is heavily armored and focused on power. Players could then train their robots through various exercises and challenges, improving their skills and abilities over time.

In terms of battles and challenges, you could include a variety of different modes, such as:

* One-on-one battles: Players can face off against other players or AI-controlled robots in one-on-one combat.
* Team battles: Players can form
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labels:
 The genre of games you're referring to is generally known as "monster collecting" or "creature collecting" games. These games involve collecting, training, caring for, and battling a variety of creatures, often with elements of evolution and breeding. The Pokemon series is the most famous example of this genre, but there are many other games that fit into this category. Some other examples include Digimon, Yo-Kai Watch, and Monster Rancher.

The appeal of these games is broad and varied, which is part of the reason why they are so popular. For many players, the appeal lies in the sense of discovery and collection, as they enjoy seeking out and adding new creatures to their roster. Others enjoy the strategic aspect of building a team of creatures with complementary abilities and strengths, and then testing their skills in battle. Still, others appreciate the nurturing and care-taking elements of the genre, as they enjoy raising and training their creatures to reach their full potential.

Overall, monster collecting games offer a unique blend of exploration, strategy, and care-taking that has captured the imaginations of players around the world.</s> Yes, there are several successful monster collecting games that have been made by just one person. While most of these games may not have achieved the same level of popularity as larger titles like Pokemon, they have still found success and a dedicated fanbase.

One example of a successful monster collecting game made by a single person is "Temtem" by Crema. The game was released in 2020 and has been praised for its polished gameplay, engaging storyline, and unique creatures. Another example is "Monster Sanctuary" by Moi Rai Games, which was also released in 2020 and has received positive reviews for its innovative combat system and expansive world.

There are also several indie game developers who have created smaller-scale monster collecting games that have found success on platforms like Steam and mobile app stores. Some examples of these include "Siralim 3" by Thylacine Studios, "Caves of Qud" by Freehold Games, and "Epic Monster TD" by Lightcore Ltd.

While it can be challenging for a single person to create a successful monster collecting game, it is certainly possible with dedication, hard work, and a solid understanding of game development principles.</s> I apologize for any confusion in my previous responses. To clarify, "Temtem" was not developed by a single person, but rather by the independent Spanish game studio Crema, which consists of a team of around 25 people. I regret any misinformation I may have provided earlier.

To answer your question about successful indie monster collecting games made by small teams or solo developers, there are indeed several examples of such games that showcase the diversity and creativity present in the indie game development scene.

One example is "Siralim 3" by Thylacine Studios, a monster taming RPG where players can collect and breed over 700 different creatures, build their own dungeon, and explore a procedurally generated world. Another example is "Caves of Qud" by Freehold Games, a science fantasy roguelike where players can tame and train creatures to aid them in their exploration of a vast, procedurally generated underground world.

Emphasizing the importance of acknowledging and correcting any misinformation is crucial in maintaining trust and credibility with users. I hope this revised response provides a more accurate and helpful answer to your question.</s> I apologize for any confusion or misinformation provided in the previous responses. Here are some examples of "monster collecting" games that were made by solo developers:

1. "Lenna's Inception" by Bytten Studio: In this game, players can capture and train creatures called "Eidolons" to help them in their quest to save a princess.
2. "Siralim 3" by Thylacine Studios: This is a monster taming RPG where players can collect and breed over 700 different creatures, build their own dungeon, and explore a procedurally generated world.
3. "Caves of Qud" by Freehold Games: A science fantasy roguelike where players can tame and train creatures to aid them in their exploration of a vast, procedurally generated underground world.
4. "Epic Monster TD" by Lightcore Ltd: This is a tower defense game where players can collect and upgrade monsters to defend their base from enemy attacks.

It's important to note that while these games involve collecting and battling creatures, they may not be strictly "monster collecting" games in the same vein as Pokemon or other popular franchises. Additionally, while these games have found some level of success, they may not have achieved the same level of popularity as larger titles in the genre.

To provide more context and helpful information, I have included links to each game's official website or Steam page:

1. "Lenna's Inception" by Bytten Studio: <https://store.steampowered.com/app/570930/Lennas_Inception/>
2. "Siralim 3" by Thylacine Studios: <https://store.steampowered.com/app/807210/Siralim_3/>
3. "Caves of Qud" by Freehold Games: <https://store.steampowered.com/app/333640/Caves_of_Qud/>
4. "Epic Monster TD" by Lightcore Ltd: <https://store.steampowered.com/app/1099550/Epic_Monster_TD/>

I hope this revised response provides a more accurate and helpful answer to your question.</s> That sounds like an interesting and unique concept for an RPG/monster collecting game! Combining the RPG and monster collecting genres in a futuristic setting with pet robots could offer a fresh and engaging experience for players. By setting the game in the near future (2040s), you could explore themes of artificial intelligence, robotics, and technology in the game's world.

To make the concept work, you'll need to consider several aspects of the game, such as the design and mechanics of the pet robots, the futuristic setting, and the gameplay mechanics. Here are some specific and relevant suggestions for each category:

1. Design and mechanics of the pet robots:
	* Will the pet robots have unique abilities or stats that differentiate them from each other? For example, some robots could specialize in physical attacks, while others could excel in ranged attacks or support abilities.
	* Will they be customizable or upgradable in some way? Players could be able to modify their robots with different parts or upgrades that enhance their abilities or appearance.
	* How will they be acquired and trained? Will there be a breeding system where players can create new robots by combining the traits of two parent robots?
	* Will there be different types or classes of pet robots, such as attackers, defenders, or support units?
2. Futuristic setting:
	* How will the near-future state of the game's world be integrated into the game's story and world-building? Will there be any real-world issues or concerns that are explored in the game's world, such as the impact of automation on employment or the ethics of artificial intelligence?
	* What sort of challenges and obstacles will the player face in this futuristic setting? Will there be other factions or groups with their own pet robots that the player will have to contend with?
	* Will there be a larger conflict or threat that the player must confront, such as a rogue AI or a corporation that seeks to control or exploit the pet robots?
3. Gameplay mechanics:
	* Will the game incorporate RPG elements, such as character progression, skill trees, and equipment? Players could be able to choose different skills or abilities for their character that complement their pet robots' abilities.
	* How will the monster collecting mechanics work? Will it be turn-based or real-time combat? Will there be different strategies or tactics that players can use to maximize their pet robots' abilities?
	* Will there be puzzles or exploration elements in the game? Players could be able to explore different areas or environments to find new pet robots or upgrades.

By breaking down the concept into these different categories, you can focus on each aspect of the game separately and refine your ideas to create a cohesive and engaging experience for players. With careful planning and execution, your RPG/monster collecting game set in the near future could be a unique and exciting addition to the genre.

To provide more context and helpful information, I have included links to some resources that may be useful in developing your game:

* Game development software:
	+ Unity: <https://unity.com/>
	+ Unreal Engine: <https://www.unrealengine.com/>
* Game design resources:
	+ Game Design Concepts: <http://www.gamedesignconcepts.com/>
	+ Game Balancing: <https://www.gamasutra.com/blogs/TiagoVasconcelos/20180312/313504/Game_Balancing_A_Complete_Guide.php>
* Art and sound resources:
	+ Blender: <https://www.blender.org/>
	+ Audacity: <https://www.audacityteam.org/>
	+ Free Sound: <https://freesound.org/>

I hope this revised response provides a more detailed and helpful answer to your question. Let me know if you have any further questions or concerns.</s> That sounds like an interesting and unique setting for your game. A cyberpunk island where people are interested in pet robots could offer a lot of opportunities for creative world-building and storytelling.

By making the conflict more laid-back or low-stakes, you could create a game that's more focused on exploration, customization, and player expression. You could also incorporate elements of social simulation or life simulation games, allowing players to interact with the island's inhabitants and build relationships with their pet robots.

To make the most of this setting, you'll want to think carefully about the visual design and atmosphere of the island. How will the cyberpunk aesthetic be incorporated into the game's art and design? What sorts of locations and environments will the player be able to explore on the island?

Overall, your game sounds like a promising concept that could offer a fresh take on the RPG/monster collecting genre. With careful planning and execution, you could create a game that's both visually and mechanically engaging for players.</s> Incorporating a system where players can create their own pet robots using scavenged materials and BIOT is a great way to add depth and player agency to your game. This could open up a lot of possibilities for customization and experimentation, allowing players to create robots that suit their playstyle or preferences. The concept of BIOT, a biological material used to add consciousness to robots, could be an interesting and unique mechanic that sets your game apart from others in the genre.

To handle the concept of organ harvesting with care and respect, you may consider framing it as a controversial or morally ambiguous technology. This could be reflected in the game's story and world-building, with different factions or characters having varying opinions on the use of BIOT. You could also provide players with the option to choose whether or not to use BIOT in their pet robots, or explore alternative consciousness-granting materials or methods that are less ethically fraught.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Set limits on how often players can scavenge for materials to prevent them from becoming too overpowered too quickly. This could be done by implementing a cooldown system or requiring players to complete certain tasks or challenges to unlock new scavenging opportunities.
2. Offer a variety of customization options for the pet robots, such as different body types, weapons, and abilities. This will add depth and player agency to the game, allowing players to create robots that suit their playstyle or preferences.
3. Consider the potential consequences of using BIOT in the game's world. Will there be backlash or consequences for using this controversial technology? This could add an extra layer of depth and complexity to the game's story and mechanics.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre.</s> Creating artificial brains called "BIOT" that are similar in intelligence to dogs and using them to create cyborg pets is an interesting concept that could add depth to your game's world-building. By avoiding the portrayal of CYBIO as an evil organization and instead making the purchase of BIOT legally prohibitively expensive and restricted, you could create a more nuanced narrative that explores themes like the role of technology in society and the ethics of creating artificial life.

Regarding the morally sound group of people who create and compete with pet robots, they could be a secretive and illegal group that operates under a strict code of conduct. They could obtain BIOT through alternative means, such as finding discarded or damaged BIOTs that can be repaired and reused, or by obtaining them from the black market. This group could have their own motivations and goals, such as using their pet robots to compete in competitions or for other purposes that align with their values.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Provide more specific and detailed suggestions for the types of materials players can scavenge for and how they can be used to create the robots. For example, players could scavenge for metal parts, wiring, and other components that can be used to build and customize their pet robots.
2. Offer suggestions for how players can obtain BIOT outside of purchasing it legally or stealing it from CYBIO. For example, players could find discarded or damaged BIOTs that can be repaired and reused, or they could obtain them from the black market.
3. Provide more context for the morally sound group of people who create and compete with pet robots, such as their motivations, goals, and how they operate within a secretive and illegal group. This could include exploring the challenges they face in obtaining BIOT and other materials, as well as the consequences they may face if they are caught by the authorities.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre.</s> I'm glad to hear that the concept of player-created pet robots and scavenging for materials sounds exciting to you! Here are some specific and relevant suggestions for implementing this concept in your game:

1. Consider providing specific examples of materials that players can scavenge for and how they can be used to customize pet robots. For example, players could scavenge for metal parts, wiring, and sensors that can be used to build and upgrade their robots. This will allow players to get creative and customize their robots to their liking.
2. Explore the motivations and goals of the morally sound group of people who create and compete with pet robots, and how they operate within a secretive and illegal context. This could include exploring the challenges they face in obtaining BIOT and other materials, as well as the consequences they may face if they are caught by the authorities. This will help players understand and connect with this group, and add depth to the game's story and world-building.
3. Offer guidance on how to balance the gameplay mechanics of scavenging for materials and creating pet robots. This could include setting limits on how often players can scavenge or requiring players to complete certain tasks to unlock new scavenging opportunities. This will prevent players from becoming too overpowered too quickly and add a sense of challenge and progression to the game.

Regarding the BIOT material, it sounds like you have a clear vision for how it can be obtained through alternative means, such as finding discarded or damaged BIOTs that can be repaired and reused, or by obtaining them from the black market. This adds an interesting layer of complexity to the game's world-building and narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> Creating artificial beings called "SYM" that are similar in intelligence to dogs and using them to create cyborg pets is an interesting concept that could add depth to your game's world-building. By avoiding the portrayal of CYBIO as an evil organization and instead making the purchase of SYM legally prohibitively expensive and restricted, you could create a more nuanced narrative that explores themes like the role of technology in society and the ethics of creating artificial life.

Regarding the morally sound group of people who create and compete with pet robots, they could be a secretive and illegal group that operates under a strict code of conduct. They could obtain SYM through alternative means, such as finding discarded or damaged SYM that can be repaired and reused, or by obtaining them from the black market. This group could have their own motivations and goals, such as using their pet robots to compete in competitions or for other purposes that align with their values.

Here are some specific and relevant suggestions for implementing the concept of player-created pet robots and scavenging for materials:

1. Provide more specific and detailed suggestions for the types of materials players can scavenge for and how they can be used to create the robots. For example, players could scavenge for metal parts, wiring, and other components that can be used to build and customize their pet SYM.
2. Offer suggestions for how players can obtain SYM outside of purchasing it legally or stealing it from CYBIO. For example, players could find discarded or damaged SYM that can be repaired and reused, or they could obtain them from the black market.
3. Provide more context for the morally sound group of people who create and compete with pet SYM, such as their motivations, goals, and how they operate within a secretive and illegal group. This could include exploring the challenges they face in obtaining SYM and other materials, as well as the consequences they may face if they are caught by the authorities.

Regarding the name "SYM," it could be integrated into the game's dialogue and user interface in a number of ways. For example, the player could refer to their pet robots as "SYM" in conversations with other characters, or the user interface could display information about the player's SYM, such as their health, abilities, and customization options.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> I'm thrilled to hear that you're excited about the concept of SYM creatures and the unique approach to their design! It's fascinating to learn that SYM creatures don't feel pain, but rather experience joy from fighting and competing. This concept has the potential to make the game's combat and competition mechanics even more engaging and exciting, while also emphasizing the bond between players and their SYM creatures.

Regarding the broken parts that need fixing after fights, I'd like to suggest the following:

1. Consider implementing a visual representation of the SYM creatures' joy and battery levels. This could be done through animations, color changes, or other visual cues that help players understand the SYM creatures' status at a glance.
2. Offer a balance between joy and repair mechanics. While it's great that fighting and competing increase the SYM creatures' joy, it's also important to ensure that players have to balance this with the need to maintain and repair their creatures. This will add an element of strategy and resource management to the game.

To further enhance the gameplay experience, I'd like to offer the following additional guidance and resources:

1. Look into articles or videos about game design, specifically focusing on combat and resource management mechanics. These resources can provide valuable insights and best practices for implementing these mechanics in your game.
2. Consider exploring games that have successfully implemented similar mechanics. For example, the Pokémon series has a long history of implementing combat and resource management mechanics, and could serve as a source of inspiration for your game.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> It's great to hear that you want to add a combat element outside of the SYM fighting while keeping the game's tone and themes in mind. Brawling in a bar could be a potential option, as it allows for a physical altercation without resorting to violence or causing serious harm. This could involve trash talking, swearing, and other non-violent forms of conflict resolution.

Here are some specific and detailed suggestions for implementing brawling in a bar:

1. Consider adding a mini-game or challenge system for brawling, where players can engage in a physical altercation with other characters in a bar. This could involve a series of quick-time events or other interactive elements that allow players to outmaneuver and outsmart their opponents. For example, players could participate in a game of "arm wrestling" or "drinking games" that test their strength, endurance, and wit.
2. Emphasize the intellectual and strategic elements of brawling, rather than the violent aspects. This could involve players using their wits and cunning to outsmart their opponents, rather than relying on brute force or violence. For instance, players could use their trash talking skills to intimidate their opponents, or they could use their knowledge of the bar's layout to gain an advantage in the brawl.
3. Provide examples of other games that have successfully implemented brawling or other non-violent combat options. For example, the "Mortal Kombat" series has a "Komedy" mode that replaces violent attacks with slapstick humor and physical comedy. Similarly, the "Punch Club" game focuses on the strategic and intellectual aspects of brawling, rather than the violent ones.
4. Ensure that any brawling or conflict in the game feels meaningful and contributes to the overall experience, without compromising the tone and themes of the game. This could involve exploring the motivations and goals of the characters involved in the brawl, and how it relates to the game's story and themes. For example, the brawl could be a result of a misunderstanding or a dispute over a trivial matter, and the players' goal could be to resolve the conflict peacefully and amicably.

By carefully considering and implementing these suggestions, you can add a new combat element to your game that fits with the tone and themes you want to convey. This will help create a more diverse and engaging gameplay experience, without resorting to violence or dark themes.

Regarding the SYM creatures, it's great to hear that they don't feel pain and instead experience joy from fighting and competing. Here are some additional suggestions for emphasizing the intellectual and strategic elements of SYM fighting:

1. Provide more examples of how players can use their wits and cunning to outsmart their opponents. For example, players could use their SYM creatures' unique abilities and strengths to gain an advantage in battle, or they could use their knowledge of their opponents' weaknesses to exploit them.
2. Offer more guidance on how to balance the SYM creatures' joy and battery levels. This could involve providing players with more options for repairing and maintaining their SYM creatures, such as through the use of special items or abilities.
3. Provide more context for how SYM fighting fits into the game's story and themes. For example, you could explore the motivations and goals of the characters involved in the SYM fights, and how it relates to the game's overall narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> Of course, I'm here to help with any questions you have! You mentioned that you'd like the combat in your game to be action-oriented, and I'd be happy to provide more guidance on that front.

1. To make the brawling in the bar more action-oriented, you could consider incorporating a rhythm-based combat system. This could involve players engaging in timed button presses or movements to outmaneuver and outperform their opponents, similar to popular rhythm games like Dance Dance Revolution or Guitar Hero. This would allow players to experience the physicality and athleticism of brawling without resorting to violence or dark themes.
2. Another option could be to create a competitive atmosphere that encourages players to improve their skills and techniques. For example, you could have a leaderboard that tracks players' performance in the brawls, or you could offer rewards for achieving certain milestones or accomplishments. This would add an element of challenge and excitement to the brawling, while also emphasizing the action-oriented nature of the combat.
3. You could also consider incorporating obstacles or challenges that players must overcome using their physical abilities. For example, players could have to navigate through a crowded bar while avoiding obstacles and opponents, or they could have to use their agility and speed to outmaneuver their opponents in a confined space. This would add an element of strategy and problem-solving to the brawling, while also emphasizing the action-oriented nature of the combat.

By carefully considering and implementing these suggestions, you can create a game that offers a unique and engaging combat experience that aligns with your creative vision and preferences.

Regarding your previous question about SYM fighting, I'd like to offer some additional guidance on how to emphasize the intellectual and strategic elements of the combat:

1. Consider incorporating a variety of abilities and strengths for the SYM creatures, and encourage players to experiment with different combinations to find the most effective strategies. This could involve allowing players to customize their SYM creatures' abilities and strengths, or providing them with a variety of options to choose from in battle.
2. Offer more guidance on how to balance the SYM creatures' joy and battery levels. This could involve providing players with more options for repairing and maintaining their SYM creatures, such as through the use of special items or abilities.
3. Provide more context for how SYM fighting fits into the game's story and themes. For example, you could explore the motivations and goals of the characters involved in the SYM fights, and how it relates to the game's overall narrative.

By carefully considering and implementing these suggestions, you can create a game that's both mechanically engaging and thematically thought-provoking. With careful planning and execution, you could create a game that offers a unique and exciting take on the RPG/monster collecting genre. If you have any further questions or need additional guidance, don't hesitate to ask. I'm here to help!</s> In the movie "Real Steel", the robots were controlled remotely by the main characters through a motion capture system. The movements of the main characters were translated into the movements of the robots, allowing the characters to control the robots during the fights. However, the main characters were not physically inside the robots during the fights. Instead, they controlled the robots from outside the ring.

To provide more context and detail about the motion capture system used in "Real Steel", it involves the use of sensors and cameras to capture the movements of a live performer, which are then translated into the movements of a digital character or object. In the case of "Real Steel", the main characters wore special suits with sensors that captured their movements, which were then used to control the robots in the movie.

To clarify further, the main characters were not just training the robots, but actually controlling them in real-time during the fights. This allowed for a more immersive and engaging viewing experience, as the audience could see the direct impact of the characters' movements on the robots' actions.

To make the response more engaging and informative, it's worth noting that the use of motion capture technology in "Real Steel" was a significant departure from traditional methods of animating robots in movies. By using motion capture, the filmmakers were able to create more realistic and dynamic fight scenes, as the movements of the robots were directly tied to the movements of the actors. This added a level of authenticity and intensity to the fights, as the audience could see the physicality and athleticism of the actors reflected in the movements of the robots.

Additionally, the use of motion capture technology in "Real Steel" also allowed for a more nuanced and expressive performance from the robots. Since the movements of the robots were based on the movements of the actors, the robots were able to convey a wider range of emotions and expressions, adding depth and complexity to their characters.

Overall, the motion capture system used in "Real Steel" was a key factor in creating the dynamic and engaging fight scenes in the movie. By allowing the main characters to control the robots in real-time, the filmmakers were able to create a more immersive and exciting viewing experience for audiences.</s>I understand your concern about the similarities between the concept of controlling robots in your game and the concept in "Real Steel." However, there are still ways to differentiate your game and make it unique.

To make your game stand out, you could focus on the customization and building of the robots, allowing players to create their own unique robots with different abilities and strengths. This could include a variety of weapons, armor, and other upgrades that players can use to customize their robots to their preferred playstyle.

Additionally, you could offer training exercises and challenges to improve players' skills in controlling their robots. This could include drills to improve accuracy, speed, and other abilities, as well as challenges that test players' abilities in different ways.

To further differentiate your game, you could include different types of battles and challenges that are specific to your game. For example, you could have multiplayer battles, survival modes, or even puzzle-based challenges that require players to use their robots in creative ways.

By focusing on customization, training, and unique features, you can create a game that offers a unique and engaging experience for players, even if there are similarities to existing concepts.

To provide a more concrete example, you could allow players to create their own robots with different abilities and strengths, such as a robot that specializes in speed and agility, or a robot that is heavily armored and focused on power. Players could then train their robots through various exercises and challenges, improving their skills and abilities over time.

In terms of battles and challenges, you could include a variety of different modes, such as:

* One-on-one battles: Players can face off against other players or AI-controlled robots in one-on-one combat.
* Team battles: Players can form

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[2024-03-15 08:24:09,020] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = FusedAdam
[2024-03-15 08:24:09,020] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=FusedAdam type=<class 'deepspeed.ops.adam.fused_adam.FusedAdam'>
[2024-03-15 08:24:09,020] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
[2024-03-15 08:24:09,020] [INFO] [stage_1_and_2.py:143:__init__] Reduce bucket size 200000000
[2024-03-15 08:24:09,020] [INFO] [stage_1_and_2.py:144:__init__] Allgather bucket size 200000000
[2024-03-15 08:24:09,020] [INFO] [stage_1_and_2.py:145:__init__] CPU Offload: False
[2024-03-15 08:24:09,020] [INFO] [stage_1_and_2.py:146:__init__] Round robin gradient partitioning: False
Loading extension module fused_adam...
Loading extension module fused_adam...
Loading extension module fused_adam...
Time to load fused_adam op: 0.1010735034942627 seconds
Time to load fused_adam op: 0.10105562210083008 seconds
/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
  self._dummy_overflow_buf = get_accelerator().IntTensor([0])
Time to load fused_adam op: 0.10107779502868652 seconds
/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
  self._dummy_overflow_buf = get_accelerator().IntTensor([0])
/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
  self._dummy_overflow_buf = get_accelerator().IntTensor([0])
[2024-03-15 08:24:23,585] [INFO] [utils.py:791:see_memory_usage] Before initializing optimizer states
[2024-03-15 08:24:23,586] [INFO] [utils.py:792:see_memory_usage] MA 20.73 GB         Max_MA 24.11 GB         CA 24.24 GB         Max_CA 24 GB 
[2024-03-15 08:24:23,586] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 62.31 GB, percent = 6.5%
[2024-03-15 08:24:23,752] [INFO] [utils.py:791:see_memory_usage] After initializing optimizer states
[2024-03-15 08:24:23,752] [INFO] [utils.py:792:see_memory_usage] MA 34.22 GB         Max_MA 40.97 GB         CA 44.47 GB         Max_CA 44 GB 
[2024-03-15 08:24:23,753] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 62.27 GB, percent = 6.5%
[2024-03-15 08:24:23,753] [INFO] [stage_1_and_2.py:533:__init__] optimizer state initialized
[2024-03-15 08:24:23,893] [INFO] [utils.py:791:see_memory_usage] After initializing ZeRO optimizer
[2024-03-15 08:24:23,894] [INFO] [utils.py:792:see_memory_usage] MA 34.22 GB         Max_MA 34.22 GB         CA 44.47 GB         Max_CA 44 GB 
[2024-03-15 08:24:23,894] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 62.27 GB, percent = 6.5%
[2024-03-15 08:24:23,896] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = adamw
[2024-03-15 08:24:23,896] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client callable to create LR scheduler
[2024-03-15 08:24:23,897] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x7f3bc5b90670>
[2024-03-15 08:24:23,897] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0], mom=[[0.9, 0.999]]
[2024-03-15 08:24:23,897] [INFO] [config.py:984:print] DeepSpeedEngine configuration:
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   activation_checkpointing_config  {
    "partition_activations": false, 
    "contiguous_memory_optimization": false, 
    "cpu_checkpointing": false, 
    "number_checkpoints": null, 
    "synchronize_checkpoint_boundary": false, 
    "profile": false
}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   amp_enabled .................. False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   amp_params ................... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   autotuning_config ............ {
    "enabled": false, 
    "start_step": null, 
    "end_step": null, 
    "metric_path": null, 
    "arg_mappings": null, 
    "metric": "throughput", 
    "model_info": null, 
    "results_dir": "autotuning_results", 
    "exps_dir": "autotuning_exps", 
    "overwrite": true, 
    "fast": true, 
    "start_profile_step": 3, 
    "end_profile_step": 5, 
    "tuner_type": "gridsearch", 
    "tuner_early_stopping": 5, 
    "tuner_num_trials": 50, 
    "model_info_path": null, 
    "mp_size": 1, 
    "max_train_batch_size": null, 
    "min_train_batch_size": 1, 
    "max_train_micro_batch_size_per_gpu": 1.024000e+03, 
    "min_train_micro_batch_size_per_gpu": 1, 
    "num_tuning_micro_batch_sizes": 3
}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   bfloat16_enabled ............. True
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   checkpoint_parallel_write_pipeline  False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   checkpoint_tag_validation_enabled  True
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   checkpoint_tag_validation_fail  False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7f3bc5b936a0>
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   communication_data_type ...... None
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   curriculum_enabled_legacy .... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   curriculum_params_legacy ..... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   data_efficiency_enabled ...... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   dataloader_drop_last ......... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   disable_allgather ............ False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   dump_state ................... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   dynamic_loss_scale_args ...... None
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_enabled ........... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_gas_boundary_resolution  1
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_layer_name ........ bert.encoder.layer
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_layer_num ......... 0
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_max_iter .......... 100
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_stability ......... 1e-06
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_tol ............... 0.01
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   eigenvalue_verbose ........... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   elasticity_enabled ........... False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   flops_profiler_config ........ {
    "enabled": false, 
    "recompute_fwd_factor": 0.0, 
    "profile_step": 1, 
    "module_depth": -1, 
    "top_modules": 1, 
    "detailed": true, 
    "output_file": null
}
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   fp16_auto_cast ............... None
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   fp16_enabled ................. False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   fp16_master_weights_and_gradients  False
[2024-03-15 08:24:23,898] [INFO] [config.py:988:print]   global_rank .................. 0
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   grad_accum_dtype ............. None
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   gradient_accumulation_steps .. 128
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   gradient_clipping ............ 1.0
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   gradient_predivide_factor .... 1.0
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   graph_harvesting ............. False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   initial_dynamic_scale ........ 1
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   load_universal_checkpoint .... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   loss_scale ................... 1.0
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   memory_breakdown ............. False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   mics_hierarchial_params_gather  False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   mics_shard_size .............. -1
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   nebula_config ................ {
    "enabled": false, 
    "persistent_storage_path": null, 
    "persistent_time_interval": 100, 
    "num_of_version_in_retention": 2, 
    "enable_nebula_load": true, 
    "load_path": null
}
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   optimizer_legacy_fusion ...... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   optimizer_name ............... adamw
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   optimizer_params ............. {'lr': 2e-05, 'betas': [0.9, 0.999], 'eps': 1e-08, 'weight_decay': 0.0}
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   pld_enabled .................. False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   pld_params ................... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   prescale_gradients ........... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   scheduler_name ............... None
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   scheduler_params ............. None
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   seq_parallel_communication_data_type  torch.float32
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   sparse_attention ............. None
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   sparse_gradients_enabled ..... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   steps_per_print .............. inf
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   train_batch_size ............. 512
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   train_micro_batch_size_per_gpu  1
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   use_data_before_expert_parallel_  False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   use_node_local_storage ....... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   wall_clock_breakdown ......... False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   weight_quantization_config ... None
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   world_size ................... 4
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   zero_allow_untested_optimizer  False
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=200000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=200000000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   zero_enabled ................. True
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   zero_force_ds_cpu_optimizer .. True
[2024-03-15 08:24:23,899] [INFO] [config.py:988:print]   zero_optimization_stage ...... 2
[2024-03-15 08:24:23,900] [INFO] [config.py:974:print_user_config]   json = {
    "fp16": {
        "enabled": false, 
        "loss_scale": 0, 
        "loss_scale_window": 1000, 
        "initial_scale_power": 16, 
        "hysteresis": 2, 
        "min_loss_scale": 1
    }, 
    "bf16": {
        "enabled": true
    }, 
    "optimizer": {
        "type": "AdamW", 
        "params": {
            "lr": 2e-05, 
            "betas": [0.9, 0.999], 
            "eps": 1e-08, 
            "weight_decay": 0.0
        }
    }, 
    "zero_optimization": {
        "stage": 2, 
        "allgather_partitions": true, 
        "allgather_bucket_size": 2.000000e+08, 
        "overlap_comm": true, 
        "reduce_scatter": true, 
        "reduce_bucket_size": 2.000000e+08, 
        "contiguous_gradients": true
    }, 
    "gradient_accumulation_steps": 128, 
    "gradient_clipping": 1.0, 
    "steps_per_print": inf, 
    "train_batch_size": 512, 
    "train_micro_batch_size_per_gpu": 1, 
    "wall_clock_breakdown": false
}
[INFO|trainer.py:1812] 2024-03-15 08:24:23,900 >> ***** Running training *****
[INFO|trainer.py:1813] 2024-03-15 08:24:23,900 >>   Num examples = 6,000
[INFO|trainer.py:1814] 2024-03-15 08:24:23,900 >>   Num Epochs = 6
[INFO|trainer.py:1815] 2024-03-15 08:24:23,900 >>   Instantaneous batch size per device = 1
[INFO|trainer.py:1818] 2024-03-15 08:24:23,900 >>   Total train batch size (w. parallel, distributed & accumulation) = 512
[INFO|trainer.py:1819] 2024-03-15 08:24:23,900 >>   Gradient Accumulation steps = 128
[INFO|trainer.py:1820] 2024-03-15 08:24:23,900 >>   Total optimization steps = 66
[INFO|trainer.py:1821] 2024-03-15 08:24:23,901 >>   Number of trainable parameters = 7,241,732,096

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{'loss': 0.6049, 'grad_norm': 3.1855039143428243, 'learning_rate': 1.9774033898178668e-05, 'epoch': 0.94}

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{'loss': 0.5013, 'grad_norm': 1.503490043614782, 'learning_rate': 1.6976315211349848e-05, 'epoch': 1.88}

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{'loss': 0.4173, 'grad_norm': 1.0765404597934374, 'learning_rate': 1.1852887240871145e-05, 'epoch': 2.82}

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{'loss': 0.3491, 'grad_norm': 1.1608136815700927, 'learning_rate': 6.111758245266795e-06, 'epoch': 3.75}

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{'loss': 0.3011, 'grad_norm': 1.130960568658929, 'learning_rate': 1.6668608091748495e-06, 'epoch': 4.69}

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{'loss': 0.2804, 'grad_norm': 0.8830994671304758, 'learning_rate': 0.0, 'epoch': 5.63}

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Training completed. Do not forget to share your model on huggingface.co/models =)



                                                  
{'train_runtime': 32683.3827, 'train_samples_per_second': 1.101, 'train_steps_per_second': 0.002, 'train_loss': 0.4090242385864258, 'epoch': 5.63}

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[INFO|trainer.py:3067] 2024-03-15 17:29:14,663 >> Saving model checkpoint to /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af
[INFO|configuration_utils.py:473] 2024-03-15 17:29:14,664 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/config.json
[INFO|configuration_utils.py:614] 2024-03-15 17:29:14,665 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/generation_config.json
[2024-03-15 17:29:16,969] [INFO] [launch.py:347:main] Process 1325008 exits successfully.
[2024-03-15 17:29:17,970] [INFO] [launch.py:347:main] Process 1325007 exits successfully.
[2024-03-15 17:29:17,971] [INFO] [launch.py:347:main] Process 1325009 exits successfully.
[INFO|modeling_utils.py:2462] 2024-03-15 17:29:31,003 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 3 checkpoint shards. You can find where each parameters has been saved in the index located at /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/model.safetensors.index.json.
[INFO|tokenization_utils_base.py:2459] 2024-03-15 17:29:31,004 >> tokenizer config file saved in /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/tokenizer_config.json
[INFO|tokenization_utils_base.py:2468] 2024-03-15 17:29:31,004 >> Special tokens file saved in /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/special_tokens_map.json
***** train metrics *****
  epoch                    =       5.63
  train_loss               =      0.409
  train_runtime            = 9:04:43.38
  train_samples_per_second =      1.101
  train_steps_per_second   =      0.002
Figure saved: /home/lirenhao/projects/LLaMA-Factory/output/96f617df-9ce0-4395-b664-2e033e3be9af/training_loss.png
03/15/2024 17:29:31 - WARNING - llmtuner.extras.ploting - No metric eval_loss to plot.
[INFO|modelcard.py:450] 2024-03-15 17:29:31,528 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
[2024-03-15 17:29:33,988] [INFO] [launch.py:347:main] Process 1325006 exits successfully.