This model uses the Evol-Instruct-Code-80k-v1 dataset generated using the Evol-Teacher repo. Currently, WizardCoder is one the most performant Code Generation models, being beaten only by ChatGPT. This takes the Code Alpaca 20k dataset and evolves each instruction through a randomly chosen evolution prompt to increase instruction complexity. These prompts range from increase time/space complexity, to increasing requirements, to adding erroneus code to improve robustness, etc. This is done three times with pruning and post processing to remove unwanted instructions and responses. The iterative addition of more complexity gives higher quality and more in-depth instructions than what is ususally generated in Alpaca methods. This, like in the case of WizardCoder and WizardLM, can lead to strong performance that gets very close to RLHF model performance.
This model uses ReplitLM fine tuned with the following parameters:
--model_name_or_path replit/replit-code-v1-3b \
--data_path ./data/EvolInstruct-Code-80k/EvolInstruct-Code-80k.json \
--output_dir ./checkpoints \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50 \
--save_total_limit 2 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--model_max_length 2000 \
--bf16 True \
--tf32 True
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