wehub-resource-sync
50 lines
1.5 KiB
Bash
50 lines
1.5 KiB
Bash
# External vLLM
|
|
|
|
# Assume we have two nodes, one with 8 GPUs of 80GB each (880G) and another with 2 GPUs of 80GB each (2 80G).
|
|
# NODE1. The node with 2*80G will be used to deploy the vLLM server.
|
|
# NODE2. The node with 8*80G will be used for full-parameter fine-tuning of the 32B model.
|
|
|
|
# Note : Use beta=0 to disable the reference model; otherwise, it may lead to Out-of-Memory (OOM) errors.
|
|
|
|
# NODE1 for vLLM Server
|
|
CUDA_VISIBLE_DEVICES=0,1 \
|
|
swift rollout \
|
|
--model Qwen/Qwen2.5-32B-Instruct \
|
|
--vllm_tensor_parallel_size 2
|
|
|
|
# NODE2 for Training
|
|
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
|
|
NPROC_PER_NODE=8 \
|
|
swift rlhf \
|
|
--rlhf_type grpo \
|
|
--model Qwen/Qwen2.5-32B-Instruct \
|
|
--reward_funcs accuracy \
|
|
--use_vllm true \
|
|
--vllm_mode server \
|
|
--vllm_server_host xxx \
|
|
--vllm_server_port 8000 \
|
|
--tuner_type full \
|
|
--torch_dtype bfloat16 \
|
|
--dataset AI-MO/NuminaMath-TIR#1000 \
|
|
--load_from_cache_file true \
|
|
--max_completion_length 2048 \
|
|
--num_train_epochs 3 \
|
|
--per_device_train_batch_size 1 \
|
|
--per_device_eval_batch_size 1 \
|
|
--learning_rate 1e-6 \
|
|
--gradient_accumulation_steps 1 \
|
|
--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 \
|
|
--top_p 0.9 \
|
|
--top_k 50 \
|
|
--deepspeed zero3 \
|
|
--log_completions true \
|
|
--num_iterations 1 \
|
|
--report_to tensorboard wandb \
|
|
--beta 0.0
|