mirror of
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68 lines
1.3 KiB
YAML
68 lines
1.3 KiB
YAML
# check ./tina/utils/constant.py
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model_post_train_dataset_name: curated_limr
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model_post_train_type: grpo
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rl_post_train_reward_funcs:
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- format
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- accuracy
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rl_post_train_reward_weights:
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- 1.0
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- 2.0
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# Model configs from trl
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model_name_or_path: DeepSeek-R1-Distill-Qwen-1.5B
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attn_implementation: flash_attention_2
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use_peft: true
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lora_r: 32
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lora_alpha: 128
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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- down_proj
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- up_proj
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- gate_proj
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# GRPO trainer configs from trl
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bf16: true
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use_vllm: true
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vllm_device: cuda:0
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vllm_gpu_memory_utilization: 0.4
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vllm_max_model_len: 4608
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gradient_accumulation_steps: 4
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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log_level: info
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logging_first_step: true
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logging_steps: 1
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logging_strategy: steps
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push_to_hub: false
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hub_strategy: every_save
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hub_private_repo: true
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hub_model_id: TODO
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learning_rate: 1e-06
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lr_scheduler_type: cosine_with_min_lr
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lr_scheduler_kwargs:
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min_lr_rate: 0.1
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max_prompt_length: 512
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max_completion_length: 3584
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max_steps: 360 # use 360 for lr scheduler but stop at 180 steps
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num_generations: 4
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num_train_epochs: 1
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overwrite_output_dir: true
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per_device_train_batch_size: 4
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report_to:
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- wandb
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save_strategy: steps
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save_steps: 10
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save_total_limit: 100
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seed: 42
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temperature: 0.7
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warmup_ratio: 0.1
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