mirror of
https://github.com/deepseek-ai/DeepSeek-Coder
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44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
## How to Fine-tune DeepSeek-Coder
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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 -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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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-6.7b-instruct`.
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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.
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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-6.7b-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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``` |