GPTChat

GPTChat provides conversation modeling CLI based on GPT-2.

This repository uses GPT-2 models provided by powerful and exciting OSS 🤗 Transformers by HuggingFace.

Installation

Build Docker imageb.py.

$ git clone https://github.com/noriyukipy/gptchat
$ cd gptchat
$ docker image build -t gptchat .

Change directory to working directory.

$ cd config

Tokenizer

Train your tokenizer with gptchat.tokenizer.train

$ docker container run  -v $(pwd):/work -w /work --rm -it gptchat python -m gptchat.tokenizer.train --config=tokenizer/config.yaml

LM Model

LM model is a language model which aims to be used as a pretrained model for latter models.

Preparation of Dataset

Before training, prepare language corpus such as Wikipedia.

Training

$ docker container run --gpus all -v $(pwd):/work -w /work --rm -it gptchat python -m gptchat.lm.train --config=lm/config.yaml

Serve API

$ docker container run -v $(pwd):/work -w /work -p 8000:8000 --rm -it gptchat python -m gptchat.lm.serve_api --config=lm/config.yaml --host=0.0.0.0 --port=8000

ChatLM Model

ChatLM model is a sequence to sequence model by fine-tuning pretrained GPT-2 to generate a response from a context given by a user.

Context and response are given to the model separeted by the [SEP] token. The [CLS] token is also appended at the end of the tokens.

To distinguish context and response, token_type_id is also given to the model.

	1	2	3	5	6
Token	Good	morning	[SEP]	Hi	[CLS]
Token Type IDs	0	0	1	1	1
Attention mask	1	1	1	1	1
Target or not	1	1	1	1	1

This model is trained with CrossEntropyLoss of the only response tokens.

Dataset Preparation

Prepare a chatting corpus which has two columns separated by a TAB character. First column is a context, and the second is a response to the first column.

Training

Train the model with

$ docker container run --gpus all -v $(pwd):/work -w /work --rm -it gptchat python -m gptchat.chatlm.train --config=chatlm/config.yaml

Serve API

$ docker container run -v $(pwd):/work -w /work -p 8000:8000 --rm -it gptchat python -m gptchat.chatlm.serve_api --config=chatlm/config.yaml --host=0.0.0.0 --port=8000

Logs

To monitor training progress, use tensorboard. Go to the output directory, and then run tensorboard.

$ docker container run -v $(pwd):/work -w /work -p 6006:6006 --rm gptchat tensorboard --logdir . --host=0.0.0.0