lewtun
61 linhas
1.7 KiB
YAML
61 linhas
1.7 KiB
YAML
# Model arguments
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model_name_or_path: Qwen/Qwen2.5-1.5B-Instruct
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model_revision: main
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torch_dtype: bfloat16
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attn_implementation: flash_attention_2
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# Data training arguments
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dataset_name: open-r1/ioi
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dataset_prompt_column: problem
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system_prompt: "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</answer>"
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# GRPO trainer config
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beta: 0.01
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bf16: true
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use_vllm: true
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do_eval: false
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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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hub_model_id: Qwen2.5-1.5B-Open-R1-Code-GRPO
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hub_strategy: every_save
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learning_rate: 5.0e-06
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log_completions: true
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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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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: 1024
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max_completion_length: 2048
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max_steps: 500
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num_generations: 14
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num_train_epochs: 1
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output_dir: data/Qwen2.5-1.5B-Open-R1-Code-GRPO
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overwrite_output_dir: true
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per_device_train_batch_size: 16
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push_to_hub: true
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report_to:
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- wandb
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save_strategy: "steps"
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save_steps: 50
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save_total_limit: 1
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seed: 42
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temperature: 1.0
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warmup_ratio: 0.03
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# ioi specific config
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code_language: cpp
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reward_funcs:
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- ioi_code
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- code_format
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- format
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reward_weights:
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- 1.0
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- 0.1
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- 0.1
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# for each generation, evaluate these many test cases in parallel, then check if any of them failed (0 score): if so stop evaluating
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# otherwise continue with the next batch of test cases. Useful to avoid overloading the eval server + save time on wrong solutions
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code_eval_test_batch_size: 3 |