resnet-classifier/data/config.yaml

84 lines
1.2 KiB
YAML

weights_format: sft
models:
- name: "classifier"
tokens: 0
len: 6
dim: 512
resnet: 34
#loras:
#- name : "lora"
# rank: 128
# alpha: 128
# training: True
# rvq_levels: []
hyperparameters:
batch_size: 256
gradient_accumulation_steps: 1
gradient_clipping: 1.0
warmup_steps: 10
optimizer: Prodigy
learning_rate: 1.0
torch_optimizer: True
scheduler: "" # ScheduleFree
torch_scheduler: True
evaluation:
batch_size: 64
frequency: 100
size: 64
steps: 450
temperature: 0.0
trainer:
iterations: 1_000_000
save_frequency: 100
keep_last_checkpoints: 32
check_for_oom: False
gradient_checkpointing: True
weight_dtype: bfloat16
amp: True
backend: deepspeed
deepspeed:
inferencing: False
amp: False
inference:
backend: local
weight_dtype: bfloat16
amp: True
optimizations:
injects: False
replace: True
linear: False
embedding: False
optimizers: True
bitsandbytes: False
dadaptation: False
bitnet: False
fp8: False
dataset:
use_hdf5: True
hdf5_flag: r
workers: 1
cache: True
training: [
"./data/images/"
]
validation: [
"./data/validation/"
]