wehub-resource-sync
55 lines
1.8 KiB
Bash
55 lines
1.8 KiB
Bash
# On-Policy Distillation https://thinkingmachines.ai/blog/on-policy-distillation/
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#
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# NOTE: When the student is a base model and the teacher is an instruct model,
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# they use different EOS tokens (e.g. Qwen3-Base uses <|endoftext|> while
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# Qwen3-Instruct uses <|im_end|>). Training with reverse KL (beta=1) directly
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# will cause the student's EOS probability to drop, leading to length explosion.
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#
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# Following the blog's approach, you should SFT the base model first to teach it
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# the instruct format (including the correct EOS token), then run on-policy
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# distillation on the SFT checkpoint. For example:
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#
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# swift sft --model Qwen/Qwen3-8B-Base \
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# --dataset open-thoughts/OpenThoughts3-1.2M \
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# --output_dir output/sft_checkpoint ...
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#
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# Then replace --model below with the SFT checkpoint path.
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# CUDA_VISIBLE_DEVICES=7 \
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# swift rollout \
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# --model Qwen/Qwen3-8B-Base \
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# --vllm_max_model_len 24192
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NPROC_PER_NODE=7 \
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PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6 \
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swift rlhf \
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--rlhf_type grpo \
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--model Qwen/Qwen3-8B-Base \
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--teacher_model Qwen/Qwen3-32B \
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--tuner_type full \
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--dataset open-thoughts/OpenThoughts3-1.2M#10000 \
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--torch_dtype bfloat16 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 1 \
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--learning_rate 1e-5 \
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--gradient_accumulation_steps 1 \
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--num_generations 1 \
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--save_steps 1000 \
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--save_total_limit 2 \
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--logging_steps 1 \
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--max_length 16000 \
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--max_completion_length 8192 \
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--output_dir output \
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--warmup_ratio 0.05 \
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--save_only_model true \
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--dataloader_num_workers 64 \
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--dataset_num_proc 4 \
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--deepspeed zero2 \
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--teacher_deepspeed zero3 \
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--attn_impl flash_attn \
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--use_vllm true \
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--vllm_mode server \
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--vllm_server_host 127.0.0.1 \
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--vllm_server_port 8000
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