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#!/bin/bash
# GPUS_PER_NODE=8
# NNODES=1
# NODE_RANK=0
# MASTER_ADDR=localhost
# MASTER_PORT=6001
MODEL="/data1/speech/anhnmt2/Speech2Speech/half-streaming-speech-nlp/checkpoints/minicpmo_sft_asr"
TOKENIZER_PATH="/data1/speech/anhnmt2/Speech2Speech/half-streaming-speech-nlp/omni_speech/model/minicpmo/MiniCPM-o-2_6"
# or openbmb/MiniCPM-V-2, openbmb/MiniCPM-Llama3-V-2_5, openbmb/MiniCPM-V-2_6
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
# See the section for finetuning in README for more information.
DATA="/data1/speech/anhnmt2/dataset/s2s/minicpmo/asr/train_asr_mixed_500k.jsonl"
EVAL_DATA="/data1/speech/anhnmt2/dataset/s2s/minicpmo/asr/dev_asr_mixed.jsonl"
# if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm, if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE="llama3",
# if use openbmb/MiniCPM-o-2_6 or openbmb/MiniCPM-V-2_6, please set LLM_TYPE=qwen
LLM_TYPE="qwen"
MODEL_MAX_Length=2048 # if conduct multi-images sft, please set MODEL_MAX_Length=4096
# DISTRIBUTED_ARGS="
# --nproc_per_node $GPUS_PER_NODE \
# --nnodes $NNODES \
# --node_rank $NODE_RANK \
# --master_addr $MASTER_ADDR \
# --master_port $MASTER_PORT
# "
deepspeed ../omni_speech/train/train_minicpmo.py \
--deepspeed zero2.json \
--model_name_or_path $MODEL \
--tokenizer_path $TOKENIZER_PATH \
--llm_type $LLM_TYPE \
--data_path $DATA \
--eval_data_path $EVAL_DATA \
--remove_unused_columns false \
--label_names "labels" \
--prediction_loss_only false \
--bf16 true \
--do_train \
--do_eval \
--tune_speech true \
--tune_llm false \
--model_max_length $MODEL_MAX_Length \
--eval_steps 2000 \
--output_dir ../checkpoints/minicpmo_sft_asr \
--num_train_epochs 2 \
--logging_strategy "steps" \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "steps" \
--save_strategy "steps" \
--save_steps 5000 \
--save_total_limit 1 \
--learning_rate 1e-5 \
--max_grad_norm 20. \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--gradient_checkpointing true |