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91 lines
3.2 KiB
Markdown
91 lines
3.2 KiB
Markdown
## How to Fine-tune DeepSeek-Coder-V2
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We provide script `finetune_deepseekcoder.py` for users to finetune our models on downstream tasks.
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The script supports the training with [DeepSpeed](https://github.com/microsoft/DeepSpeed). You need install required packages by:
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```bash
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pip install flash-attn --no-build-isolation
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pip install -r requirements.txt
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```
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Please follow [Sample Dataset Format](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) to prepare your training data.
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You can download the sample dataset from [HuggingFace](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) by:
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```bash
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wget https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/resolve/main/EvolInstruct-Code-80k.json
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```
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Each line is a json-serialized string with two required fields `instruction` and `output`.
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After data preparation, you can use the sample shell script to finetune `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`. Remember to specify `DATA_PATH`, `OUTPUT_PATH`.
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And please choose appropriate hyper-parameters(e.g., `learning_rate`, `per_device_train_batch_size`) according to your scenario. For devices supported by flash_attention, you can refer [here](https://github.com/Dao-AILab/flash-attention).
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For this configuration, zero_stage needs to be set to 3.
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```bash
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DATA_PATH="<your_data_path>"
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OUTPUT_PATH="<your_output_path>"
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MODEL_PATH="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
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deepspeed finetune_deepseekcoder.py \
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--model_name_or_path $MODEL_PATH \
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--data_path $DATA_PATH \
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--output_dir $OUTPUT_PATH \
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--num_train_epochs 3 \
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--model_max_length 1024 \
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--per_device_train_batch_size 16 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 4 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 100 \
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--save_total_limit 100 \
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--learning_rate 2e-5 \
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--warmup_steps 10 \
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--logging_steps 1 \
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--lr_scheduler_type "cosine" \
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--gradient_checkpointing True \
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--report_to "tensorboard" \
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--deepspeed configs/ds_config_zero3.json \
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--bf16 True \
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--use_lora False
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```
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You can also finetune the model with 4/8-bits qlora, feel free to try it. For this configuration, it is possible to run on a single A100 80G GPU, and adjustments can be made according to your resources.
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```bash
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DATA_PATH="<your_data_path>"
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OUTPUT_PATH="<your_output_path>"
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MODEL_PATH="<your_model_path>"
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deepspeed finetune.py \
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--model_name_or_path $MODEL_PATH \
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--data_path $DATA_PATH \
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--output_dir $OUTPUT_PATH \
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--num_train_epochs 3 \
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--model_max_length 1024 \
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--per_device_train_batch_size 16 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 4 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 100 \
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--save_total_limit 100 \
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--learning_rate 2e-5 \
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--warmup_steps 10 \
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--logging_steps 1 \
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--lr_scheduler_type "cosine" \
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--gradient_checkpointing True \
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--report_to "tensorboard" \
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--deepspeed configs/ds_config_zero2_no_offload.json \
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--bf16 True \
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--use_lora True \
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--bits 4 \
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--max_grad_norm 0.3 \
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--double_quant \
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--lora_r 64 \
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--lora_alpha 16 \
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--quant_type nf4 \
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```
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