How can we improve the performance of our 32B parameter.
Information history:
Size	Eval Results	Train Results	Config	
7b	"{
    ""predict_bleu-4"": 55.89089500000001,
    ""predict_model_preparation_time"": 0.0026,
    ""predict_rouge-1"": 51.12250900000001,
    ""predict_rouge-2"": 32.243705999999996,
    ""predict_rouge-l"": 40.948665,
    ""predict_runtime"": 168.0939,
    ""predict_samples_per_second"": 0.595,
    ""predict_steps_per_second"": 0.018
}"	"{
    ""epoch"": 2.98961937716263,
    ""eval_loss"": 0.8236607313156128,
    ""eval_runtime"": 33.3403,
    ""eval_samples_per_second"": 21.865,
    ""eval_steps_per_second"": 0.93,
    ""num_input_tokens_seen"": 24059336,
    ""total_flos"": 1.0210506389871084e+18,
    ""train_loss"": 0.8517664337047824,
    ""train_runtime"": 10804.8862,
    ""train_samples_per_second"": 3.845,
    ""train_steps_per_second"": 0.04
}"	"bf16: true
cutoff_len: 1024
dataset: acts_cot
dataset_dir: data
ddp_timeout: 180000000
do_train: true
enable_liger_kernel: true
eval_steps: 10
eval_strategy: steps
finetuning_type: lora
flash_attn: auto
gradient_accumulation_steps: 4
include_num_input_tokens_seen: true
learning_rate: 0.0001
logging_steps: 5
lora_alpha: 16
lora_dropout: 0
lora_rank: 8
lora_target: q_proj,v_proj
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_samples: 500000
model_name_or_path: Qwen/Qwen2.5-7B-Instruct
num_train_epochs: 3.0
optim: adamw_torch
output_dir: saves/Qwen2.5-7B-Instruct/lora/train_2024-09-25-10-55-54-v4
packing: false
per_device_eval_batch_size: 24
per_device_train_batch_size: 24
plot_loss: true
preprocessing_num_workers: 16
quantization_bit: 4
quantization_method: bitsandbytes
report_to: none
save_steps: 10
stage: sft
template: qwen
val_size: 0.05
warmup_steps: 10"	
32-v1	"{
    ""predict_bleu-4"": 58.349607999999996,
    ""predict_model_preparation_time"": 0.0136,
    ""predict_rouge-1"": 52.883734999999994,
    ""predict_rouge-2"": 34.631225,
    ""predict_rouge-l"": 43.000611000000006,
    ""predict_runtime"": 418.8429,
    ""predict_samples_per_second"": 0.239,
    ""predict_steps_per_second"": 0.007
}"	"{
    ""epoch"": 2.988480526604498,
    ""num_input_tokens_seen"": 22863968,
    ""total_flos"": 4.397072693194457e+18,
    ""train_loss"": 0.6360728469364093,
    ""train_runtime"": 30863.3065,
    ""train_samples_per_second"": 1.417,
    ""train_steps_per_second"": 0.022
}"	"bf16: true
cutoff_len: 1024
dataset: acts_cot
dataset_dir: data
ddp_timeout: 180000000
do_train: true
finetuning_type: lora
flash_attn: auto
gradient_accumulation_steps: 8
include_num_input_tokens_seen: true
learning_rate: 5.0e-05
logging_steps: 5
lora_alpha: 16
lora_dropout: 0
lora_rank: 8
lora_target: all
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_samples: 100000
model_name_or_path: Qwen/Qwen2.5-32B-Instruct
num_train_epochs: 3.0
optim: adamw_torch
output_dir: saves/Qwen2.5-32B-Instruct/lora/train_2024-09-24-17-38-00-32b-4bit
packing: false
per_device_train_batch_size: 8
plot_loss: true
preprocessing_num_workers: 16
quantization_bit: 4
quantization_method: bitsandbytes
report_to: none
save_steps: 10
stage: sft
template: qwen
warmup_steps: 0"	
32-v2	"{
    ""predict_bleu-4"": 39.58476999999999,
    ""predict_model_preparation_time"": 0.0058,
    ""predict_rouge-1"": 36.29595100000001,
    ""predict_rouge-2"": 19.699915,
    ""predict_rouge-l"": 24.153225,
    ""predict_runtime"": 490.2148,
    ""predict_samples_per_second"": 0.204,
    ""predict_steps_per_second"": 0.008
}"	"{
    ""epoch"": 2.98961937716263,
    ""eval_loss"": 1.0745229721069336,
    ""eval_runtime"": 140.1509,
    ""eval_samples_per_second"": 5.202,
    ""eval_steps_per_second"": 0.221,
    ""num_input_tokens_seen"": 24059336,
    ""total_flos"": 4.618482676450836e+18,
    ""train_loss"": 1.0954694322965763,
    ""train_runtime"": 27054.9013,
    ""train_samples_per_second"": 1.536,
    ""train_steps_per_second"": 0.008
}"	"bf16: true
cutoff_len: 1024
dataset: acts_cot
dataset_dir: data
ddp_timeout: 180000000
do_train: true
enable_liger_kernel: true
eval_steps: 10
eval_strategy: steps
finetuning_type: lora
flash_attn: auto
gradient_accumulation_steps: 8
include_num_input_tokens_seen: true
learning_rate: 5.0e-06
logging_steps: 5
lora_alpha: 16
lora_dropout: 0
lora_rank: 8
lora_target: q_proj,v_proj
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_samples: 500000
model_name_or_path: Qwen/Qwen2.5-32B-Instruct
num_train_epochs: 3.0
optim: adamw_torch
output_dir: saves/Qwen2.5-32B-Instruct/lora/train_2024-09-25-19-48-21
packing: false
per_device_eval_batch_size: 24
per_device_train_batch_size: 24
plot_loss: true
preprocessing_num_workers: 16
quantization_bit: 4
quantization_method: bitsandbytes
report_to: none
save_steps: 10
stage: sft
template: qwen
val_size: 0.05
warmup_steps: 10"