# SFT Qwen2.5-Coder-0.5B Validator-Selection (v_s) — 0.5B base per user constraint.
model_name_or_path: /home/datht/huggingface/Qwen/Qwen2.5-Coder-0.5B-Instruct
model_revision: main
torch_dtype: bfloat16
use_flash_attention_2: false
response_template: "<|im_start|>assistant"

dataset_mixer:
  ../data/multi-agents/fixed/sft-validator-selection-v3: 1.0
dataset_splits:
- train
- test
preprocessing_num_workers: 24
chat_template: "{{'<|im_start|>user\n' + messages['prompt'] + '<|im_end|>\n'}}{{'<|im_start|>assistant\n' + messages['completion'] + '<|im_end|>\n'}}"

bf16: true
do_eval: false
eval_strategy: "no"
gradient_accumulation_steps: 4
gradient_checkpointing: true
learning_rate: 2.0e-05
log_level: info
logging_steps: 10
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 5120
truncation_side: left
max_steps: -1
num_train_epochs: 2
optim: adamw_torch
output_dir: output/qwen-coder0.5b-bird-validator-selection-sft-v3
overwrite_output_dir: true
per_device_train_batch_size: 8
push_to_hub: false
remove_unused_columns: true
report_to:
- tensorboard
save_strategy: "epoch"
save_total_limit: 1
seed: 42
tf32: true