99 lines
2.7 KiB
YAML
99 lines
2.7 KiB
YAML
mode: train
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experiment:
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dataset_size: 6000
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dataset_seed: 1234
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test_size: 0.1
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hf_token: ${oc.env:HF_TOKEN,null}
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output:
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root_path: ${oc.env:ROOT_PATH}
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run_name: ${model.trim}_${task.name}_${algorithm.name}_${algorithm.training.curriculum_schedule}_${algorithm.training.scheduler_params.mu_exp}_${algorithm.training.scheduler_params.sigma}_SEC${algorithm.training.scheduler_params.vrex_adds.sec}DRO${algorithm.training.scheduler_params.vrex_adds.groupdro}G${algorithm.training.scheduler_params.vrex_adds.gaussian}_minp${algorithm.training.scheduler_params.min_prob}${ckpt2short:${algorithm.training.resume_from_checkpoint}}_${algorithm.training.max_steps}
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lora:
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r: 32
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alpha: 64
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dropout: 0.1
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target_modules:
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- q_proj
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- v_proj
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task_type: CAUSAL_LM
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occupy_gpu_memory: false
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occupy_gpu_memory_gb: 50
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gpu_device: cuda:0
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model:
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family: Qwen
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trim: Qwen2.5-1.5B-Instruct
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name: ${model.family}/${model.trim}
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trust_remote_code: true
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torch_dtype: bfloat16
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attn_implementation: flash_attention_2
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task:
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name: gsm8k
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data_files:
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- datasets/gsm8k/trivial
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- datasets/gsm8k/easy
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- datasets/gsm8k/medium
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- datasets/gsm8k/hard
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training:
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data_files:
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- datasets/gsm8k/trivial
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- datasets/gsm8k/easy
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- datasets/gsm8k/medium
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- datasets/gsm8k/hard
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max_prompt_length: 1600
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max_completion_length: 1024
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inference:
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max_model_len: 3200
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checkpoint: outputs/Qwen2.5-1.5B-Instruct_gsm8k_grpo_gaussian_0.25_0.75_SEC0.3DRO1.0G0.0_minpTrue_1600/checkpoint-1600
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data_files:
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- datasets/gsm8k/trivial
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- datasets/gsm8k/easy
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- datasets/gsm8k/medium
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- datasets/gsm8k/hard
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max_prompt_length: 1600
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max_completion_length: 512
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temperature: 0
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'n': 1
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sc_num: 1
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use_icl: false
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max_new_tokens: 1600
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max_tokens: 1600
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algorithm:
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name: grpo
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training:
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resume_from_checkpoint: null
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learning_rate: 1.0e-06
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lr_scheduler_type: cosine
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logging_steps: 10
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max_steps: 1600
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per_device_train_batch_size: 16
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generation_batch_size: null
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steps_per_generation: 1
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gradient_accumulation_steps: 4
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gradient_checkpointing: true
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bf16: true
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report_to:
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- wandb
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push_to_hub: true
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save_strategy: steps
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save_steps: ${algorithm.training.max_steps}
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tf32: true
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num_generations: 8
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beta: 0.001
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use_vllm: true
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vllm_mode: colocate
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vllm_gpu_memory_utilization: 0.3
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vllm_server_port: 8000
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curriculum: false
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curriculum_schedule: gaussian
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scheduler_params:
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mu_exp: 0.25
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sigma: 0.75
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vrex_adds:
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groupdro: 1.0
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gaussian: 0.0
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sec: 0.3
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beta: 1.0
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min_prob: true
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td_alpha: 0.5
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sec_temperature: 0.3
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max_dapo_iter: 4
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