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README.md

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+---
+language: zh
+datasets: CLUECorpusSmall
+widget: 
+- text: "米饭是一种用稻米与水煮成的食物"
+
+
+---
+
+
+# Chinese GPT2-distil Model
+
+## Model description
+
+The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-distil-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-distil-chinese-cluecorpussmall). The model is called GPT2-distil because the configuration of model follows [distilgpt2](https://huggingface.co/distilgpt2), which has 6 layers, 768 dimension, and 12 heads. The pre-training does not involve the supervision of larger models.
+
+## How to use
+
+You can use the model directly with a pipeline for text generation:
+
+```python
+>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
+>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-distil-chinese-cluecorpussmall")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-distil-chinese-cluecorpussmall")
+>>> text_generator = TextGenerationPipeline(model, tokenizer)   
+>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
+    [{'generated_text': '这是很久之前的事情了 。 我 现 在 想 起 来 就 让 自 己 很 伤 心 ， 很 失 望 。 我 现 在 想 到 ， 我 觉 得 大 多 数 人 的 生 活 比 我 的 生 命 还 要 重 要 ， 对 一 些 事 情 的 看 法 ， 对 一 些 人 的 看 法 ， 都 是 在 发 泄 。 但 是 ， 我 们 的 生 活 是 需 要 一 个 信 用 体 系 的 。 我 不 知'}]
+```
+
+## Training data
+
+[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 
+
+## Training procedure
+
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 1024. 
+
+Stage1:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
+                      --seq_length 128 --processes_num 32 --data_processor lm 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --config_path models/gpt2/distil_config.json \
+                    --output_model_path models/cluecorpussmall_gpt2_distil_seq128_model.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
+                    --learning_rate 1e-4 --batch_size 64
+```
+
+Stage2:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
+                      --seq_length 1024 --processes_num 32 --data_processor lm 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --pretrained_model_path models/cluecorpussmall_gpt2_distil_seq128_model.bin-1000000 \
+                    --config_path models/gpt2/distil_config.json \
+                    --output_model_path models/cluecorpussmall_gpt2_distil_seq1024_model.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
+                    --learning_rate 5e-5 --batch_size 16
+```
+
+Finally, we convert the pre-trained model into Huggingface's format:
+
+```
+python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_distil_seq1024_model.bin-250000 \
+                                                        --output_model_path pytorch_model.bin \
+                                                        --layers_num 6
+```
+
+### BibTeX entry and citation info
+
+```
+@article{radford2019language,
+  title={Language Models are Unsupervised Multitask Learners},
+  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
+  year={2019}
+}
+
+@article{zhao2019uer,
+  title={UER: An Open-Source Toolkit for Pre-training Models},
+  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
+  journal={EMNLP-IJCNLP 2019},
+  pages={241},
+  year={2019}
+}
+```