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📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]
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## News [2023/01/04] 🔥 We released **WizardCoder-33B-V1.1** trained from deepseek-coder-33b-base, the **SOTA OSS Code LLM** on [EvalPlus Leaderboard](https://evalplus.github.io/leaderboard.html), achieves **79.9 pass@1** on HumanEval, **73.2 pass@1** on HumanEval-Plus, **78.9 pass@1** on MBPP, and **66.9 pass@1** on MBPP-Plus. [2023/01/04] 🔥 **WizardCoder-33B-V1.1** outperforms **ChatGPT 3.5**, **Gemini Pro**, and **DeepSeek-Coder-33B-instruct** on HumanEval and HumanEval-Plus pass@1. [2023/01/04] 🔥 **WizardCoder-33B-V1.1** is comparable with **ChatGPT 3.5**, and surpasses **Gemini Pro** on MBPP and MBPP-Plus pass@1. | Model | Checkpoint | Paper | HumanEval | HumanEval+ | MBPP | MBPP+ | License | | ----- |------| ---- |------|-------| ----- | ----- |----- | | GPT-4-Turbo (Nov 2023) | - | - | 85.4 | 81.7 | 83.0 | 70.7 |-| | GPT-4 (May 2023) | - | - | 88.4 | 76.8 | - | - |-| | GPT-3.5-Turbo (Nov 2023) | - | - | 72.6 | 65.9 | 81.7 | 69.4 |-| | Gemini Pro | - | - | 63.4 | 55.5 | 72.9 | 57.9 |-| | DeepSeek-Coder-33B-instruct | - | - | 78.7 | 72.6 | 78.7 | 66.7 |-| | **WizardCoder-33B-V1.1** | 🤗 HF Link | 📃 [WizardCoder] | 79.9 | 73.2 | 78.9 | 66.9 | MSFTResearch | | WizardCoder-Python-34B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 73.2 | 64.6 | 73.2 | 59.9 | Llama2 | | WizardCoder-15B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 59.8 | 52.4 | -- | -- | OpenRAIL-M | | WizardCoder-Python-13B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 64.0 | -- | -- | -- | Llama2 | | WizardCoder-Python-7B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 55.5 | -- | -- | -- | Llama2 | | WizardCoder-3B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 34.8 | -- | -- | -- | OpenRAIL-M | | WizardCoder-1B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 23.8 | -- | -- | -- | OpenRAIL-M | ## ❗ Data Contamination Check: Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on HumanEval and MBPP test set. 🔥 ❗Note for model system prompts usage: Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**. **Default version:** ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" ``` ## How to Reproduce the Performance of WizardCoder-33B-V1.1 We provide all codes [here](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder/src). We also provide all generated [results](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/data/humaneval_mbpp_wizardcoder33b_v1.1_results.zip). ``` transformers==4.36.2 vllm==0.2.5 ``` (1) HumanEval and HumanEval-Plus - Step 1 Code Generation (w/o accelerate) ```bash model="WizardLM/WizardCoder-33B-V1.1" temp=0.0 max_len=2048 pred_num=1 num_seqs_per_iter=1 output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode mkdir -p ${output_path} echo 'Output path: '$output_path echo 'Model to eval: '$model # 164 problems, 21 per GPU if GPU=8 index=0 gpu_num=8 for ((i = 0; i < $gpu_num; i++)); do start_index=$((i * 21)) end_index=$(((i + 1) * 21)) gpu=$((i)) echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu} ((index++)) ( CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --greedy_decode ) & if (($index % $gpu_num == 0)); then wait; fi done ``` Code Generation (w/ vllm accelerate) ```bash model="WizardLM/WizardCoder-33B-V1.1" temp=0.0 max_len=2048 pred_num=1 num_seqs_per_iter=1 output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm mkdir -p ${output_path} echo 'Output path: '$output_path echo 'Model to eval: '$model CUDA_VISIBLE_DEVICES=0,1,2,3 python humaneval_gen_vllm.py --model ${model} \ --start_index 0 --end_index 164 --temperature ${temp} \ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --num_gpus 4 --overwrite ``` - Step 2: Get the score Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark. ```bash git clone https://github.com/evalplus/evalplus.git cd evalplus export PYTHONPATH=$PYTHONPATH:$(pwd) pip install -r requirements.txt ``` Get HumanEval and HumanEval-Plus scores. ```bash output_path=preds/T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode echo 'Output path: '$output_path python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt evalplus.evaluate --dataset humaneval --samples ${output_path}.jsonl ``` (2) MBPP and MBPP-Plus The preprocessed questions are provided in [mbppplus.json](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/data/mbppplus.json). - Step 1 Code Generation (w/o accelerate) ```bash model="WizardLM/WizardCoder-33B-V1.1" temp=0.0 max_len=2048 pred_num=1 num_seqs_per_iter=1 output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode mkdir -p ${output_path} echo 'Output path: '$output_path echo 'Model to eval: '$model # 399 problems, 50 per GPU if GPU=8 index=0 gpu_num=8 for ((i = 0; i < $gpu_num; i++)); do start_index=$((i * 50)) end_index=$(((i + 1) * 50)) gpu=$((i)) echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu} ((index++)) ( CUDA_VISIBLE_DEVICES=$gpu python mbppplus_gen.py --model ${model} \ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --greedy_decode ) & if (($index % $gpu_num == 0)); then wait; fi done ``` Code Generation (w/ vllm accelerate) ```bash model="WizardLM/WizardCoder-33B-V1.1" temp=0.0 max_len=2048 pred_num=1 num_seqs_per_iter=1 output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm mkdir -p ${output_path} echo 'Output path: '$output_path echo 'Model to eval: '$model CUDA_VISIBLE_DEVICES=0,1,2,3 python mbppplus_gen_vllm.py --model ${model} \ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --num_gpus 4 ``` - Step 2: Get the score Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark. ```bash git clone https://github.com/evalplus/evalplus.git cd evalplus export PYTHONPATH=$PYTHONPATH:$(pwd) pip install -r requirements.txt ``` Get HumanEval and HumanEval-Plus scores. ```bash output_path=preds/MBPP_T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode echo 'Output path: '$output_path python mbppplus_process_preds.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt evalplus.evaluate --dataset mbpp --samples ${output_path}.jsonl ``` ## Citation Please cite the repo if you use the data, method or code in this repo. ``` @article{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin}, journal={arXiv preprint arXiv:2306.08568}, year={2023} } ```