README.md · LoneStriker/WizardCoder-33B-V1.1-6.0bpw-h6-exl2 at main

metadata

metrics:
  - code_eval
library_name: transformers
tags:
  - code
model-index:
  - name: WizardCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.799
            verified: false

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

🏠 Home Page

🤗 HF Repo •🐱 Github Repo • 🐦 Twitter

📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

👋 Join our Discord

News

[2023/01/04] 🔥 We released WizardCoder-33B-V1.1 trained from deepseek-coder-33b-base, the SOTA OSS Code LLM on EvalPlus Leaderboard, 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.

We also provide all generated results.

transformers==4.36.2
vllm==0.2.5

(1) HumanEval and HumanEval-Plus

Step 1

Code Generation (w/o accelerate)

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)

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 benchmark.

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.

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.

Step 1

Code Generation (w/o accelerate)

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)

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 benchmark.

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.

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}
}