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benchmarks:
batch_size: '32'
targets:
- scope
dataset:
directory: ./tmp/data/mdcath/hf_dataset/mdcath_split/
filter_on_column:
replica:
- '0'
- '1'
- '2'
- '3'
- '4'
temperature:
- '320'
metadata:
adapter_name: ProtProfileMD_LoRA
inference: false
log_path: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/logs
name: ProtProfileMD
run_id: ProtProfileMD_20251113_072928_large_batch_size_again
version: 1.0
weights: null
model:
base_model: Rostlab/ProstT5
base_model_kwargs:
output_loading_info: false
use_safetensors: true
loss_function: kldiv
loss_function_kwargs:
reduction: batchmean
profile_head: linear
profile_head_kwargs:
dropout: 0.1
hidden_size: 1024
num_classes: 20
tokenizer:
tokenizer_model: Rostlab/ProstT5
tokenizer_model_kwargs:
do_lower_case: false
legacy: false
use_fast: true
training:
data_collator:
pad_to_multiple_of: 8
padding: true
ddp: true
devices: 0,1,2,3
lora:
bias: none
inference_mode: false
lora_alpha: 16
lora_dropout: 0.05
modules_to_save:
- profile_head
r: 8
target_modules:
- q
- v
use_dora: false
use_rslora: false
quantize: null
save_dir: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/model
training_args:
batch_eval_metrics: false
eval_on_start: true
eval_steps: 32
eval_strategy: steps
gradient_accumulation_steps: 10
label_names:
- profiles
learning_rate: 0.001
logging_steps: 1
logging_strategy: steps
lr_scheduler_type: cosine
num_train_epochs: 6
output_dir: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/training
per_device_eval_batch_size: 32
per_device_train_batch_size: 5
remove_unused_columns: true
report_to: wandb
save_steps: 300
save_strategy: steps
save_total_limit: 16
seed: 42
warmup_steps: 200
wandb:
project: protprofilemd
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