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Lewis Tunstall
2023-11-09 07:32:24 +00:00
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## Supervised Fine-Tuning (SFT)
We provide 3 main ways to train SFT models:
* Distributed fine-tuning of all model weights with ZeRO-3
* Fine-tuning with LoRA adapters and ZeRO-3
* Fine-tuning with QLoRA adapters and DDP
```shell
# Full training with ZeRO-3
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/{model_name}/sft/config_full.yaml
# LoRA training with ZeRO-3
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/{model_name}/sft/config_16bit.yaml
# QLoRA training with DDP
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml scripts/run_sft.py recipes/{model_name}/sft/config_8bit.yaml
```
You can override the parameters in each YAML config by appending them to the command as follows:
```shell
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/{model_name}/sft/config_full.yaml --per_device_train_batch_size=2 --num_train_epochs=3
```
## Direct Preference Optimisation (DPO)
```shell
# Full training with ZeRO-3
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py recipes/{model_name}/dpo/config_full.yaml
# LoRA training with ZeRO-3
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py recipes/{model_name}/dpo/config_16bit.yaml
# QLoRA training with DDP
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml scripts/run_dpo.py recipes/{model_name}/dpo/config_8bit.yaml
```