wehub-resource-sync
50 lines
1.5 KiB
Bash
50 lines
1.5 KiB
Bash
SYSTEM_PROMPT="You are a helpful math assistant. Solve the problem step by step. Show your reasoning in <think> </think> tags, then give the final numerical answer after ####.
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For example:
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<think> ... reasoning ... </think>
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#### 42"
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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/Qwen2.5-3B-Instruct \
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--external_plugins examples/train/grpo/plugin/gsm8k/gsm8k_plugin.py \
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--reward_funcs gsm8k_accuracy gsm8k_format \
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--columns '{"answer": "solution"}' \
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--enable_thinking false \
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--use_vllm true \
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--vllm_mode colocate \
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--vllm_gpu_memory_utilization 0.4 \
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--vllm_tensor_parallel_size 1 \
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--vllm_max_model_len 10240 \
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--vllm_enable_lora true \
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--sleep_level 1 \
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--tuner_type lora \
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--quant_method bnb \
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--quant_bits 4 \
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--bnb_4bit_quant_type nf4 \
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--torch_dtype bfloat16 \
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--dataset 'modelscope/gsm8k' \
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--load_from_cache_file true \
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--max_length 2048 \
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--max_completion_length 8192 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 4 \
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--learning_rate 1e-5 \
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--lr_scheduler_type cosine \
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--save_steps 10 \
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--save_total_limit 100 \
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--logging_steps 1 \
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--warmup_ratio 0.0 \
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--dataloader_num_workers 4 \
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--num_generations 8 \
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--temperature 1.0 \
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--system "$SYSTEM_PROMPT" \
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--deepspeed zero2 \
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--log_completions true \
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--report_to tensorboard swanlab \
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--max_grad_norm 1.0 \
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--epsilon 0.2 \
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--epsilon_high 0.28 \
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--scale_rewards none
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