31 lines
1.3 KiB
YAML
31 lines
1.3 KiB
YAML
project: AgentScope-ReAct
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name: Learn_to_Ask-Qwen2.5-7B-fixed
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# directory to save checkpoints, default to ./checkpoints if TRINITY_CHECKPOINT_ROOT_DIR not set
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checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
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algorithm:
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algorithm_type: grpo # a GRPO-based algorithm for multi-step reasoning
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model:
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# path to the pre-trained model, default to Qwen/Qwen2.5-7B-Instruct if TRINITY_MODEL_PATH not set
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model_path: ${oc.env:TRINITY_MODEL_PATH,Qwen/Qwen2.5-7B-Instruct}
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tinker: # tinker config, you can set tinker parameters here
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enable: false # if true, tinker will be enabled
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cluster:
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node_num: 1 # cluster with 1 node
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gpu_per_node: 8 # each node has 8 GPUs
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buffer:
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total_epochs: 4 # run taskset for 4 epoch
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explorer:
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runner_per_model: 32 # each model has 32 runners for parallel rollout
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max_timeout: 600 # max timeout for each rollout is 600 seconds
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synchronizer:
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sync_style: fixed
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sync_method: 'nccl'
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sync_interval: 10
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sync_timeout: 7200 # wait for 120 minutes
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trainer:
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save_interval: 90 # save checkpoint every 90 steps
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use_dynamic_bsz: true
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ulysses_sequence_parallel_size: 1 # use sequence parallelism to reduce memory usage
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monitor:
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monitor_type: wandb # here we use wandb; you can also use tensorboard, mlflow or swanlab
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