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---
license: cc-by-sa-4.0
datasets:
- chargoddard/rpguild
language:
- en
---
# RPGPT
GPT2 model trained on Role Playing datset.
## Custom Tokens
The model containes 4 custom tokens to diffirentiate between Character, Context and Input data.
The Expected input to the model is therefore:
```python
"<|CHAR|> Character Info <|CONTEXT|> Dialog or generation context <|INPUT|> User input"
```
The model is trained to include Response token to what we consider responce.
Meaning the model output will be:
```python
"<|CHAR|> Character Info <|CONTEXT|> Dialog or generation context <|INPUT|> User input <|RESPONSE|> Model Response"
```
The actual output can be extracted by split function
```python
model_out = "<|CHAR|> Character Info <|CONTEXT|> Dialog or generation context <|INPUT|> User input <|RESPONSE|> Model Response".split('<|RESPONSE|>')[-1]
```
## Usage
For more easy use, cosider downloading scripts from my repo https://github.com/jinymusim/DialogSystem
Then use the included classes as follows.
```python
from utils.dialog_model import DialogModel
from transformers import AutoTokenizer
model = DialogModel('jinymusim/RPGPT', resize_now=False)
tok = AutoTokenizer.from_pretrained('jinymusim/RPGPT')
tok.model_max_length = 1024
char_name ="James Smith"
bio="Age: 30, Gender: Male, Hobies: Training language models"
model.set_character(char_name, bio)
print(model.generate_self(tok)) # For Random generation
print(model.generate(tok, input("USER>").strip())) # For user input converasion
```
Other wise use standard huggingface interface
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM('jinymusim/RPGPT')
tok = AutoTokenizer.from_pretrained('jinymusim/RPGPT')
tok.model_max_length = 1024
char_name ="James Smith"
bio="Age: 30, Gender: Male, Hobies: Training language models"
context = []
input_ids = tok.encode(f"<|CHAR|> {char_name}, Bio: {bio} <|CONTEXT|> {' '.join(context} <|INPUT|> {input('USER>')}")
response_out = model.generate(input_ids,
max_new_tokens= 150,
do_sample=True,
top_k=50,
early_stopping=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id)
print(response_out)
```
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