Update README.md

README.md

@@ -47,6 +47,7 @@ model-index:
 |  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/deepseek-ai/deepseek-coder-6.7b-base/blob/main/LICENSE" target="_blank">Deepseek</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>  |
@@ -74,9 +75,164 @@ Please use **the same systems prompts strictly** with us, and we do not guarante
 
 ## How to Reproduce the Performance of WizardCoder-33B-V1.1
 
-We provide all codes [here]().
+We provide all codes [here](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder/src).
 
+```
+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