wehub-resource-sync
53 lines
1.8 KiB
Bash
Executable File
53 lines
1.8 KiB
Bash
Executable File
# ============================================================
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# Swift GRPO training with OpenEnv TextArena Sudoku
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#
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# Prerequisites:
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# 1. Start Sudoku server (separate terminal):
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# TEXTARENA_ENV_ID=Sudoku-v0 MAX_CONCURRENT_ENVS=8 \
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# python examples/train/grpo/plugin/openenv/start_sudoku_server.py
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#
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# 2. This script uses colocate mode:
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# - vLLM and training share the same GPUs
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# - No separate rollout server needed
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#
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# Environment: TextArena Sudoku (local server, port 8000)
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# Model: Qwen3.5-4B (enable_thinking=false)
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# Scheduler: SudokuScheduler (multi-turn, content diff tracking)
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# Multi-turn: max_turns=20 (20 moves per game)
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# Rewards: 5-component (empty_cell/valid_move/repetition/progress/correct)
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# Hints: Board parsing + guaranteed moves + candidates
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#
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# ============================================================
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CUDA_VISIBLE_DEVICES=0,1,2,3 \
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NPROC_PER_NODE=4 \
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swift rlhf \
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--rlhf_type grpo \
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--model Qwen/Qwen3.5-4B \
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--dataset examples/train/grpo/plugin/openenv/sudoku.jsonl#1000 \
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--external_plugins examples/train/grpo/plugin/openenv/sudoku_scheduler.py \
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--enable_thinking false \
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--torch_dtype bfloat16 \
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--max_completion_length 256 \
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--max_length 8192 \
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--learning_rate 5e-6 \
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--num_train_epochs 3 \
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--per_device_train_batch_size 1 \
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--num_generations 4 \
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--generation_batch_size 4 \
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--gradient_accumulation_steps 4 \
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--temperature 1 \
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--use_vllm true \
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--vllm_mode colocate \
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--vllm_max_model_len 12288 \
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--vllm_gpu_memory_utilization 0.35 \
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--gradient_checkpointing true \
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--use_gym_env true \
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--multi_turn_scheduler sudoku_scheduler \
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--max_turns 20 \
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--save_strategy steps \
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--save_steps 50 \
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--logging_steps 1 \
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--log_completions true \
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--report_to tensorboard swanlab
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