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mftcoder_accelerate/README.md

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@@ -193,7 +193,13 @@ accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate
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or
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FSDP config in command line arguments
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```bash
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sh ds_single_launch.sh
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sh fsdp_single_launch.sh
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```
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#### MultiNode Launch
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Refer to the deepspeed multi-node launch script below.
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```bash
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sh ds_multinode_launch.sh
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```
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#### Traing Arguments

mftcoder_accelerate/README_cn.md

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@@ -146,7 +146,7 @@ QLoRA通过4-bit的nf4量化,且加入更多adapter,在大幅减少显存消
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QLoRA论文指出,该方法可以在一张V100上对33B的模型进行微调,并且性能逼近全量参数微调。
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执行如下命令即可进行 Lora/QLora/全量 微调:
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#### Launch via Deepspeed
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#### Deepspeed 单机启动
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DeepSpeed配置在accelerate_ds_config.yaml中。
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```bash
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accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "DeepSpeed"
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sh ds_zero3_single_launch.sh
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```
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#### Launch via FSDP
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#### FSDP 单机启动
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FSDP配置在accelerate_fsdp_config.yaml中。
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```bash
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accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "FSDP"
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sh fsdp_single_launch.sh
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```
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#### 多机启动
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多机启动请参考如下deepspeed多机启动脚本
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```bash
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sh ds_multinode_launch.sh
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```
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#### 训练参数
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_**训练需要的参数配置在```configs/*_train_config```中,主要参数说明如下:**_
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