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README.md ADDED
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+ ---
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+ metrics:
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+ - code_eval
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+ library_name: transformers
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+ tags:
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+ - code
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+ model-index:
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+ - name: WizardCoder
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+ results:
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+ - task:
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+ type: text-generation
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+ dataset:
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+ type: openai_humaneval
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+ name: HumanEval
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+ metrics:
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+ - name: pass@1
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+ type: pass@1
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+ value: 0.799
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+ verified: false
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+ ---
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+
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+ ## WizardCoder: Empowering Code Large Language Models with Evol-Instruct
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+
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+ <p style="font-size:28px;" align="center">
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+ 🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
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+ <p align="center">
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+ <p align="center">
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+ 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> </p>
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+ <p align="center">
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+ 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
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+ </p>
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+ <p align="center">
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+ 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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+ </p>
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+
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+ ## News
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+
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+ [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.
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+
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+ [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.
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+
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+ [2023/01/04] 🔥 **WizardCoder-33B-V1.1** is comparable with **ChatGPT 3.5**, and surpasses **Gemini Pro** on MBPP and MBPP-Plus pass@1.
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+
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+ | Model | Checkpoint | Paper | HumanEval | HumanEval+ | MBPP | MBPP+ | License |
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+ | ----- |------| ---- |------|-------| ----- | ----- |----- |
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+ | GPT-4-Turbo (Nov 2023) | - | - | 85.4 | 81.7 | 83.0 | 70.7 |-|
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+ | GPT-4 (May 2023) | - | - | 88.4 | 76.8 | - | - |-|
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+ | GPT-3.5-Turbo (Nov 2023) | - | - | 72.6 | 65.9 | 81.7 | 69.4 |-|
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+ | Gemini Pro | - | - | 63.4 | 55.5 | 72.9 | 57.9 |-|
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+ | DeepSeek-Coder-33B-instruct | - | - | 78.7 | 72.6 | 78.7 | 66.7 |-|
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+ | **WizardCoder-33B-V1.1** | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-33B-V1.1" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 79.9 | 73.2 | 78.9 | 66.9 | <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.1/resolve/main/LICENSE" target="_blank">MSFTResearch</a> |
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+ | WizardCoder-Python-34B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 64.6 | 73.2 | 59.9 | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
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+ | WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 | 52.4 | -- | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
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+ | WizardCoder-Python-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | -- | -- | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
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+ | WizardCoder-Python-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | -- | -- | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
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+ | WizardCoder-3B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 | -- | -- | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
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+ | WizardCoder-1B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 | -- | -- | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
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+
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+
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+ ## ❗ Data Contamination Check:
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+
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+ 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.
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+
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+ 🔥
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+ ❗<b>Note for model system prompts usage:</b>
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+
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+ Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**.
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+
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+ **Default version:**
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+
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+ ```
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+ "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
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+ ```
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+
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+
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+ ## How to Reproduce the Performance of WizardCoder-33B-V1.1
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+
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+ We provide all codes [here](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder/src).
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+
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+ We also provide all generated [results](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/data/humaneval_mbpp_wizardcoder33b_v1.1_results.zip).
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+
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+ ```
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+ transformers==4.36.2
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+ vllm==0.2.5
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+ ```
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+
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+ (1) HumanEval and HumanEval-Plus
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+
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+ - Step 1
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+
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+ Code Generation (w/o accelerate)
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+ ```bash
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+ model="WizardLM/WizardCoder-33B-V1.1"
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+ temp=0.0
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+ max_len=2048
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+ pred_num=1
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+ num_seqs_per_iter=1
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+
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+ output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode
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+
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+ mkdir -p ${output_path}
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+ echo 'Output path: '$output_path
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+ echo 'Model to eval: '$model
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+
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+ # 164 problems, 21 per GPU if GPU=8
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+ index=0
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+ gpu_num=8
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+ for ((i = 0; i < $gpu_num; i++)); do
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+ start_index=$((i * 21))
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+ end_index=$(((i + 1) * 21))
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+
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+ gpu=$((i))
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+ echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
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+ ((index++))
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+ (
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+ CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \
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+ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
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+ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --greedy_decode
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+ ) &
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+ if (($index % $gpu_num == 0)); then wait; fi
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+ done
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+ ```
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+
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+ Code Generation (w/ vllm accelerate)
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+ ```bash
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+ model="WizardLM/WizardCoder-33B-V1.1"
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+ temp=0.0
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+ max_len=2048
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+ pred_num=1
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+ num_seqs_per_iter=1
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+
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+ output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm
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+
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+ mkdir -p ${output_path}
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+ echo 'Output path: '$output_path
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+ echo 'Model to eval: '$model
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+
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+ CUDA_VISIBLE_DEVICES=0,1,2,3 python humaneval_gen_vllm.py --model ${model} \
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+ --start_index 0 --end_index 164 --temperature ${temp} \
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+ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --num_gpus 4 --overwrite
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+ ```
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+
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+ - Step 2: Get the score
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+
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+ Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark.
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+ ```bash
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+ git clone https://github.com/evalplus/evalplus.git
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+ cd evalplus
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+ export PYTHONPATH=$PYTHONPATH:$(pwd)
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+ pip install -r requirements.txt
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+ ```
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+ Get HumanEval and HumanEval-Plus scores.
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+ ```bash
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+ output_path=preds/T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode
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+
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+ echo 'Output path: '$output_path
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+ python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
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+
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+ evalplus.evaluate --dataset humaneval --samples ${output_path}.jsonl
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+ ```
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+
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+ (2) MBPP and MBPP-Plus
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+
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+ The preprocessed questions are provided in [mbppplus.json](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/data/mbppplus.json).
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+
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+ - Step 1
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+
168
+ Code Generation (w/o accelerate)
169
+ ```bash
170
+ model="WizardLM/WizardCoder-33B-V1.1"
171
+ temp=0.0
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+ max_len=2048
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+ pred_num=1
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+ num_seqs_per_iter=1
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+
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+ output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode
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+
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+ mkdir -p ${output_path}
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+ echo 'Output path: '$output_path
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+ echo 'Model to eval: '$model
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+
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+ # 399 problems, 50 per GPU if GPU=8
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+ index=0
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+ gpu_num=8
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+ for ((i = 0; i < $gpu_num; i++)); do
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+ start_index=$((i * 50))
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+ end_index=$(((i + 1) * 50))
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+
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+ gpu=$((i))
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+ echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
191
+ ((index++))
192
+ (
193
+ CUDA_VISIBLE_DEVICES=$gpu python mbppplus_gen.py --model ${model} \
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+ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
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+ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --greedy_decode
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+ ) &
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+ if (($index % $gpu_num == 0)); then wait; fi
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+ done
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+ ```
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+
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+ Code Generation (w/ vllm accelerate)
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+ ```bash
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+ model="WizardLM/WizardCoder-33B-V1.1"
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+ temp=0.0
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+ max_len=2048
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+ pred_num=1
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+ num_seqs_per_iter=1
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+
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+ output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm
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+
211
+ mkdir -p ${output_path}
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+ echo 'Output path: '$output_path
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+ echo 'Model to eval: '$model
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+
215
+ CUDA_VISIBLE_DEVICES=0,1,2,3 python mbppplus_gen_vllm.py --model ${model} \
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+ --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
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+ --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --num_gpus 4
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+ ```
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+
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+ - Step 2: Get the score
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+
222
+ Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark.
223
+ ```bash
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+ git clone https://github.com/evalplus/evalplus.git
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+ cd evalplus
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+ export PYTHONPATH=$PYTHONPATH:$(pwd)
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+ pip install -r requirements.txt
228
+ ```
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+ Get HumanEval and HumanEval-Plus scores.
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+ ```bash
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+ output_path=preds/MBPP_T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode
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+
233
+ echo 'Output path: '$output_path
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+ python mbppplus_process_preds.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
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+
236
+ evalplus.evaluate --dataset mbpp --samples ${output_path}.jsonl
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+ ```
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+
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+
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+ ## Citation
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+
242
+ Please cite the repo if you use the data, method or code in this repo.
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+
244
+ ```
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+ @article{luo2023wizardcoder,
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+ title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
247
+ 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},
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+ journal={arXiv preprint arXiv:2306.08568},
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+ year={2023}
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+ }
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+ ```
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45
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46
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47
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48
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49
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50
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51
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52
+ },
53
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54
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55
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56
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57
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58
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59
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60
+ },
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62
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63
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64
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65
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66
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67
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68
+ },
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70
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71
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72
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73
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74
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75
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76
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78
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81
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82
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83
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84
+ },
85
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86
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87
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88
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89
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90
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91
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92
+ },
93
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94
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95
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96
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97
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98
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99
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100
+ },
101
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102
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103
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104
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105
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106
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107
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108
+ },
109
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110
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113
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115
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116
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123
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125
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126
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128
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129
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130
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131
+ "special": false
132
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134
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137
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138
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139
+ "special": false
140
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142
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143
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144
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145
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146
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147
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148
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149
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150
+ "content": "<pad>",
151
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152
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153
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154
+ "single_word": false,
155
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156
+ },
157
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158
+ "content": "<|User|>",
159
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160
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161
+ "rstrip": false,
162
+ "single_word": false,
163
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164
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165
+ "32020": {
166
+ "content": "<|Assistant|>",
167
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168
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169
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170
+ "single_word": false,
171
+ "special": false
172
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173
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174
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175
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176
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177
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178
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179
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180
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182
+ "content": "<unk>",
183
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184
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185
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186
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187
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188
+ }
189
+ },
190
+ "bos_token": "<|begin▁of▁sentence|>",
191
+ "clean_up_tokenization_spaces": false,
192
+ "eos_token": "<|end▁of▁sentence|>",
193
+ "legacy": true,
194
+ "model_max_length": 16384,
195
+ "pad_token": "<|begin▁of▁sentence|>",
196
+ "padding_side": "right",
197
+ "sp_model_kwargs": {},
198
+ "tokenizer_class": "LlamaTokenizer",
199
+ "unk_token": "<unk>",
200
+ "use_default_system_prompt": false
201
+ }