#!/bin/bash

MODEL_PATH=/data1/speech/anhnmt2/Speech2Speech/LLaMA-Omni/models/llm/Qwen2.5-3B-Instruct
SPEECH_ENCODER=/data1/speech/anhnmt2/Speech2Speech/LLaMA-Omni/models/speech_encoder/whisper-medium
SPEECH_ADAPTER=/data1/speech/anhnmt2/Speech2Speech/half-streaming-speech-nlp/checkpoints/omni_whisper-medium_Qwen2.5-3B_pretrained-asr/speech_projector.bin
PROMPT_VERSION=qwen
DATA_PATH=/data1/speech/anhnmt2/dataset/s2s/english/qna/train_tmp.jsonl
DEV_PATH=/data1/speech/anhnmt2/dataset/s2s/english/qna/dev_tmp.jsonl
CACHE_DIR="../output/cached_sft"

deepspeed ../omni_speech/train/train_mem.py \
    --deepspeed zero2.json \
    --lora_enable True \
    --model_name_or_path $MODEL_PATH \
    --version $PROMPT_VERSION \
    --data_path $DATA_PATH \
    --dev_path $DEV_PATH \
    --cache_dir $CACHE_DIR \
    --speech_encoder $SPEECH_ENCODER  \
    --mel_size 80 \
    --speech_encoder_hidden_size 1024 \
    --speech_encoder_type whisper \
    --pretrain_speech_projector $SPEECH_ADAPTER \
    --bf16 True \
    --output_dir ../checkpoints/omni_whisper-medium_Qwen2.5-3B_pretrained-sft-lora \
    --num_train_epochs 18 \
    --per_device_train_batch_size 2 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 4 \
    --evaluation_strategy "steps" \
    --save_strategy "steps" \
    --eval_steps 1000 \
    --save_steps 1000 \
    --save_total_limit 1 \
    --learning_rate 2e-5 \
    --optim adamw_torch \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --dataloader_num_workers 8