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# pytest: disable
# Model arguments
model_name_or_path: AMD-OLMo-1B-dpo
torch_dtype: null
use_flash_attention_2: false
chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
# Data training arguments
# For definitions, see: src/h4/training/config.py
dataset_mixer:
csarron/argilla-ultrafeedback-binarized-preferences-cleaned: 1.0
dataset_splits:
- train
- test
preprocessing_num_workers: 16
# DPOTrainer arguments
bf16: true
beta: 0.01
do_eval: true
evaluation_strategy: steps
eval_steps: 100
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
hub_model_id: AMD-OLMo-1B-dpo
learning_rate: 5.0e-5
log_level: info
logging_steps: 10
lr_scheduler_type: cosine
max_length: 1024
max_prompt_length: 512
num_train_epochs: 3
optim: adamw_torch
output_dir: data/AMD-OLMo-1B-dpo
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
push_to_hub: false
save_strategy: "steps"
save_steps: 100
save_total_limit: 1
seed: 42
warmup_ratio: 0.1 |