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README.md

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+---
+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
+
+<p style="font-size:28px;" align="center">
+🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
+<p align="center">
+<p align="center">
+🤗 <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>
+<p align="center">
+ 📃 <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>
+</p>
+<p align="center">
+    👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
+</p>
+
+## News
+
+[2024/01/04] 🔥 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.
+
+[2024/01/04] 🔥 **WizardCoder-33B-V1.1** outperforms **ChatGPT 3.5**, **Gemini Pro**, and **DeepSeek-Coder-33B-instruct** on HumanEval and HumanEval-Plus pass@1.
+
+[2024/01/04] 🔥 **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**  |   🤗 <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>  |
+|  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>  |
+|  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>  |
+|  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>  |
+|  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>  |
+|  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>  |
+|  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>  |
+
+## How to Make the Training Data?
+
+Apply our [Code Evol-Instruct](https://wizardlm.github.io/WizardCoder/) on [Code-Aplaca data](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k).
+
+
+## ❗ 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. 
+
+🔥 
+❗<b>Note for model system prompts usage:</b>
+
+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}
+}
+```

wizardcoder-33b-v1.1.Q4_0.gguf

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