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Browse files- LICENSE +71 -0
- README.md +251 -0
- config.json +30 -0
- generation_config.json +6 -0
- model.safetensors.index.json +568 -0
- output-00001-of-00003.safetensors +3 -0
- output-00002-of-00003.safetensors +3 -0
- output-00003-of-00003.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +201 -0
LICENSE
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README.md
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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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## WizardCoder: Empowering Code Large Language Models with Evol-Instruct
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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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## News
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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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[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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[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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| 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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## ❗ Data Contamination Check:
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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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❗<b>Note for model system prompts usage:</b>
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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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**Default version:**
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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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## How to Reproduce the Performance of WizardCoder-33B-V1.1
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We provide all codes [here](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder/src).
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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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transformers==4.36.2
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vllm==0.2.5
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```
|
86 |
+
|
87 |
+
(1) HumanEval and HumanEval-Plus
|
88 |
+
|
89 |
+
- Step 1
|
90 |
+
|
91 |
+
Code Generation (w/o accelerate)
|
92 |
+
```bash
|
93 |
+
model="WizardLM/WizardCoder-33B-V1.1"
|
94 |
+
temp=0.0
|
95 |
+
max_len=2048
|
96 |
+
pred_num=1
|
97 |
+
num_seqs_per_iter=1
|
98 |
+
|
99 |
+
output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode
|
100 |
+
|
101 |
+
mkdir -p ${output_path}
|
102 |
+
echo 'Output path: '$output_path
|
103 |
+
echo 'Model to eval: '$model
|
104 |
+
|
105 |
+
# 164 problems, 21 per GPU if GPU=8
|
106 |
+
index=0
|
107 |
+
gpu_num=8
|
108 |
+
for ((i = 0; i < $gpu_num; i++)); do
|
109 |
+
start_index=$((i * 21))
|
110 |
+
end_index=$(((i + 1) * 21))
|
111 |
+
|
112 |
+
gpu=$((i))
|
113 |
+
echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
|
114 |
+
((index++))
|
115 |
+
(
|
116 |
+
CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \
|
117 |
+
--start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
|
118 |
+
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --greedy_decode
|
119 |
+
) &
|
120 |
+
if (($index % $gpu_num == 0)); then wait; fi
|
121 |
+
done
|
122 |
+
```
|
123 |
+
|
124 |
+
Code Generation (w/ vllm accelerate)
|
125 |
+
```bash
|
126 |
+
model="WizardLM/WizardCoder-33B-V1.1"
|
127 |
+
temp=0.0
|
128 |
+
max_len=2048
|
129 |
+
pred_num=1
|
130 |
+
num_seqs_per_iter=1
|
131 |
+
|
132 |
+
output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm
|
133 |
+
|
134 |
+
mkdir -p ${output_path}
|
135 |
+
echo 'Output path: '$output_path
|
136 |
+
echo 'Model to eval: '$model
|
137 |
+
|
138 |
+
CUDA_VISIBLE_DEVICES=0,1,2,3 python humaneval_gen_vllm.py --model ${model} \
|
139 |
+
--start_index 0 --end_index 164 --temperature ${temp} \
|
140 |
+
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --num_gpus 4 --overwrite
|
141 |
+
```
|
142 |
+
|
143 |
+
- Step 2: Get the score
|
144 |
+
|
145 |
+
Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark.
|
146 |
+
```bash
|
147 |
+
git clone https://github.com/evalplus/evalplus.git
|
148 |
+
cd evalplus
|
149 |
+
export PYTHONPATH=$PYTHONPATH:$(pwd)
|
150 |
+
pip install -r requirements.txt
|
151 |
+
```
|
152 |
+
Get HumanEval and HumanEval-Plus scores.
|
153 |
+
```bash
|
154 |
+
output_path=preds/T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode
|
155 |
+
|
156 |
+
echo 'Output path: '$output_path
|
157 |
+
python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
|
158 |
+
|
159 |
+
evalplus.evaluate --dataset humaneval --samples ${output_path}.jsonl
|
160 |
+
```
|
161 |
+
|
162 |
+
(2) MBPP and MBPP-Plus
|
163 |
+
|
164 |
+
The preprocessed questions are provided in [mbppplus.json](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/data/mbppplus.json).
|
165 |
+
|
166 |
+
- Step 1
|
167 |
+
|
168 |
+
Code Generation (w/o accelerate)
|
169 |
+
```bash
|
170 |
+
model="WizardLM/WizardCoder-33B-V1.1"
|
171 |
+
temp=0.0
|
172 |
+
max_len=2048
|
173 |
+
pred_num=1
|
174 |
+
num_seqs_per_iter=1
|
175 |
+
|
176 |
+
output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode
|
177 |
+
|
178 |
+
mkdir -p ${output_path}
|
179 |
+
echo 'Output path: '$output_path
|
180 |
+
echo 'Model to eval: '$model
|
181 |
+
|
182 |
+
# 399 problems, 50 per GPU if GPU=8
|
183 |
+
index=0
|
184 |
+
gpu_num=8
|
185 |
+
for ((i = 0; i < $gpu_num; i++)); do
|
186 |
+
start_index=$((i * 50))
|
187 |
+
end_index=$(((i + 1) * 50))
|
188 |
+
|
189 |
+
gpu=$((i))
|
190 |
+
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} \
|
194 |
+
--start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
|
195 |
+
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --greedy_decode
|
196 |
+
) &
|
197 |
+
if (($index % $gpu_num == 0)); then wait; fi
|
198 |
+
done
|
199 |
+
```
|
200 |
+
|
201 |
+
Code Generation (w/ vllm accelerate)
|
202 |
+
```bash
|
203 |
+
model="WizardLM/WizardCoder-33B-V1.1"
|
204 |
+
temp=0.0
|
205 |
+
max_len=2048
|
206 |
+
pred_num=1
|
207 |
+
num_seqs_per_iter=1
|
208 |
+
|
209 |
+
output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm
|
210 |
+
|
211 |
+
mkdir -p ${output_path}
|
212 |
+
echo 'Output path: '$output_path
|
213 |
+
echo 'Model to eval: '$model
|
214 |
+
|
215 |
+
CUDA_VISIBLE_DEVICES=0,1,2,3 python mbppplus_gen_vllm.py --model ${model} \
|
216 |
+
--start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
|
217 |
+
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --num_gpus 4
|
218 |
+
```
|
219 |
+
|
220 |
+
- Step 2: Get the score
|
221 |
+
|
222 |
+
Install [Eval-Plus](https://github.com/evalplus/evalplus) benchmark.
|
223 |
+
```bash
|
224 |
+
git clone https://github.com/evalplus/evalplus.git
|
225 |
+
cd evalplus
|
226 |
+
export PYTHONPATH=$PYTHONPATH:$(pwd)
|
227 |
+
pip install -r requirements.txt
|
228 |
+
```
|
229 |
+
Get HumanEval and HumanEval-Plus scores.
|
230 |
+
```bash
|
231 |
+
output_path=preds/MBPP_T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode
|
232 |
+
|
233 |
+
echo 'Output path: '$output_path
|
234 |
+
python mbppplus_process_preds.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
|
235 |
+
|
236 |
+
evalplus.evaluate --dataset mbpp --samples ${output_path}.jsonl
|
237 |
+
```
|
238 |
+
|
239 |
+
|
240 |
+
## Citation
|
241 |
+
|
242 |
+
Please cite the repo if you use the data, method or code in this repo.
|
243 |
+
|
244 |
+
```
|
245 |
+
@article{luo2023wizardcoder,
|
246 |
+
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},
|
248 |
+
journal={arXiv preprint arXiv:2306.08568},
|
249 |
+
year={2023}
|
250 |
+
}
|
251 |
+
```
|
config.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"LlamaForCausalLM"
|
4 |
+
],
|
5 |
+
"attention_bias": false,
|
6 |
+
"attention_dropout": 0.0,
|
7 |
+
"bos_token_id": 32013,
|
8 |
+
"eos_token_id": 32014,
|
9 |
+
"hidden_act": "silu",
|
10 |
+
"hidden_size": 7168,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 19200,
|
13 |
+
"max_position_embeddings": 16384,
|
14 |
+
"model_type": "llama",
|
15 |
+
"num_attention_heads": 56,
|
16 |
+
"num_hidden_layers": 62,
|
17 |
+
"num_key_value_heads": 8,
|
18 |
+
"pretraining_tp": 1,
|
19 |
+
"rms_norm_eps": 1e-06,
|
20 |
+
"rope_scaling": {
|
21 |
+
"factor": 4.0,
|
22 |
+
"type": "linear"
|
23 |
+
},
|
24 |
+
"rope_theta": 100000,
|
25 |
+
"tie_word_embeddings": false,
|
26 |
+
"torch_dtype": "bfloat16",
|
27 |
+
"transformers_version": "4.36.2",
|
28 |
+
"use_cache": false,
|
29 |
+
"vocab_size": 32256
|
30 |
+
}
|
generation_config.json
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 32013,
|
4 |
+
"eos_token_id": 32014,
|
5 |
+
"transformers_version": "4.36.2"
|
6 |
+
}
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,568 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
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|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
+
"total_size": 66685982720
|
4 |
+
},
|
5 |
+
"weight_map": {
|
6 |
+
"lm_head.weight": "model-00014-of-00014.safetensors",
|
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