Update README.md

README.md

@@ -10,4 +10,70 @@ language:
 
 GPT2 model trained on Role Playing datset.
 
-For use donwload the repo https://github.com/jinymusim/DialogSystem and use the `play_woth_model.py` script to play with the model.
+## 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)
+```
+