wehub-resource-sync
58 lines
2.0 KiB
Bash
58 lines
2.0 KiB
Bash
# Exp: https://github.com/modelscope/ms-swift/pull/5307#issuecomment-3219803922
|
|
# Before running this script, please run the following `swift rollout` script first
|
|
# This script is a example for multi-turn training with dynamic num of rollout outputs
|
|
# which means a trajectory of multi turn rollout is split into multiple data
|
|
# see details in thinking_tips_scheduler
|
|
# NOTE: for same trajectory, the reward is supported to be the same,
|
|
# here we use the last turn data of each trajectory to compute accuracy reward
|
|
# see details in thinking_tips reward function
|
|
|
|
# CUDA_VISIBLE_DEVICES=0 \
|
|
# swift rollout \
|
|
# --model Qwen/Qwen3-1.7B \
|
|
# --vllm_use_async_engine true \
|
|
# --multi_turn_scheduler thinking_tips_scheduler \
|
|
# --vllm_max_model_len 32768 \
|
|
# --vllm_gpu_memory_utilization 0.8 \
|
|
# --max_turns 3
|
|
|
|
CUDA_VISIBLE_DEVICES=1,2 \
|
|
NPROC_PER_NODE=2 \
|
|
swift rlhf \
|
|
--rlhf_type grpo \
|
|
--model Qwen/Qwen3-1.7B \
|
|
--tuner_type full \
|
|
--external_plugins examples/train/grpo/plugin/plugin.py \
|
|
--reward_funcs thinking_tips \
|
|
--loss_scale last_round \
|
|
--use_vllm true \
|
|
--vllm_mode server \
|
|
--vllm_server_host 127.0.0.1 \
|
|
--vllm_server_port 8000 \
|
|
--vllm_server_pass_dataset true \
|
|
--torch_dtype bfloat16 \
|
|
--dataset AI-MO/NuminaMath-TIR#10000 \
|
|
--load_from_cache_file true \
|
|
--split_dataset_ratio 0 \
|
|
--max_completion_length 8192 \
|
|
--num_train_epochs 1 \
|
|
--per_device_train_batch_size 2 \
|
|
--learning_rate 1e-6 \
|
|
--gradient_accumulation_steps 4 \
|
|
--steps_per_generation 8 \
|
|
--gradient_checkpointing_kwargs '{"use_reentrant": false}' \
|
|
--save_total_limit 2 \
|
|
--logging_steps 1 \
|
|
--warmup_ratio 0.05 \
|
|
--dataloader_num_workers 4 \
|
|
--dataset_num_proc 4 \
|
|
--num_generations 8 \
|
|
--temperature 1.0 \
|
|
--deepspeed zero2 \
|
|
--log_completions true \
|
|
--log_entropy true \
|
|
--importance_sampling_level sequence \
|
|
--top_entropy_quantile 0.2 \
|
|
--num_iterations 1 \
|
|
--report_to tensorboard swanlab
|