54 lines
2.4 KiB
YAML
54 lines
2.4 KiB
YAML
# Please refer to https://modelscope.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html for detailed explanation of each field.
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project: AgentScope
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name: GSM8K-Qwen3-0.6B
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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: multi_step_grpo # a GRPO-based algorithm for multi-step reasoning
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repeat_times: 8 # repeat each training sample 8 times
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model:
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# path to the pre-trained model, default to Qwen/Qwen3-0.6B if TRINITY_MODEL_PATH not set
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model_path: ${oc.env:TRINITY_MODEL_PATH,Qwen/Qwen3-0.6B}
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# maximum tokens generated in response
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max_response_tokens: 16384
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# maximum token length for both input and output
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# if you face OOM, try to reduce max_model_len and max_response_tokens
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max_model_len: 24576
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temperature: 1.0
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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: 1 # run taskset for 1 epoch
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batch_size: 32 # each step contains 32 samples from taskset
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train_batch_size: 256 # trainer batch size is 256 (multi-step reasoning generate more training samples)
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explorer_input:
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taskset: # define the taskset for rollout
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name: gsm8k
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path: 'openai/gsm8k'
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subset_name: 'main'
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split: 'train'
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explorer:
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runner_per_model: 16 # each model has 16 runners for parallel rollout
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max_timeout: 600 # max timeout for each rollout is 600 seconds
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rollout_model:
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engine_num: 4 # setup 4 vllm inference model instances
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tensor_parallel_size: 1 # each model instance uses tensor parallel size of 1
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enable_openai_api: true # some parameters to provide openai-style API, don't change them
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enable_history: true
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enable_auto_tool_choice: true
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# Qwen3 series tool_call_parser and reasoning_parser, if you use other models, please adjust accordingly
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tool_call_parser: hermes
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reasoning_parser: deepseek_r1
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synchronizer:
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sync_style: dynamic_by_explorer
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sync_method: 'nccl'
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sync_interval: 1
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sync_timeout: 1800 # wait for 30 minutes
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trainer:
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save_interval: 100 # save checkpoint every 100 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: tensorboard # here we use tensorboard, you can also use wandb, mlflow or swanlab
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