[model_cards] DialoGPT: How to use + thumbnail + conversational tag

cc @dreasysnail
Co-Authored-By: Patrick von Platen <patrick.v.platen@gmail.com>

model_cards/microsoft/DialoGPT-large/README.md

+---
+thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
+tags:
+- conversational
+---
+
 ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
 
 DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
@@ -18,3 +24,30 @@ The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
 Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
 
 ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
+
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-large")
+
+# Let's chat for 5 lines
+for step in range(5):
+	# encode the new user input, add the eos_token and return a tensor in Pytorch
+	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+
+	# append the new user input tokens to the chat history
+	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+	# generated a response while limiting the total chat history to 1000 tokens, 
+	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+	# pretty print last ouput tokens from bot
+	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+```

model_cards/microsoft/DialoGPT-medium/README.md

+---
+thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
+tags:
+- conversational
+---
+
 ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
 
 DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
@@ -18,3 +24,30 @@ The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
 Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
 
 ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
+
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-medium")
+
+# Let's chat for 5 lines
+for step in range(5):
+	# encode the new user input, add the eos_token and return a tensor in Pytorch
+	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+
+	# append the new user input tokens to the chat history
+	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+	# generated a response while limiting the total chat history to 1000 tokens, 
+	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+	# pretty print last ouput tokens from bot
+	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+```

model_cards/microsoft/DialoGPT-small/README.md

+---
+thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
+tags:
+- conversational
+---
+
 ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
 
 DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
@@ -18,3 +24,30 @@ The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
 Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
 
 ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
+
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-small")
+
+# Let's chat for 5 lines
+for step in range(5):
+	# encode the new user input, add the eos_token and return a tensor in Pytorch
+	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+
+	# append the new user input tokens to the chat history
+	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+	# generated a response while limiting the total chat history to 1000 tokens, 
+	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+	# pretty print last ouput tokens from bot
+	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+```