Instructions to use ScottaStrong/DialogGPT-medium-Scott with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ScottaStrong/DialogGPT-medium-Scott with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ScottaStrong/DialogGPT-medium-Scott")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ScottaStrong/DialogGPT-medium-Scott")
model = AutoModelForCausalLM.from_pretrained("ScottaStrong/DialogGPT-medium-Scott")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ScottaStrong/DialogGPT-medium-Scott with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ScottaStrong/DialogGPT-medium-Scott"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ScottaStrong/DialogGPT-medium-Scott",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ScottaStrong/DialogGPT-medium-Scott

SGLang

How to use ScottaStrong/DialogGPT-medium-Scott with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ScottaStrong/DialogGPT-medium-Scott" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ScottaStrong/DialogGPT-medium-Scott",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ScottaStrong/DialogGPT-medium-Scott" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ScottaStrong/DialogGPT-medium-Scott",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ScottaStrong/DialogGPT-medium-Scott with Docker Model Runner:
```
docker model run hf.co/ScottaStrong/DialogGPT-medium-Scott
```

DialogGPT-medium-Scott / README.md

ScottaStrong

Create README.md

e57ac22 almost 5 years ago

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	---
	thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
	tags:
	- conversational
	license: mit
	---
	# DialoGPT Trained on the Speech of a Game Character
	This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
	I built a Discord AI chatbot based on this model. [Check out my GitHub repo.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
	Chat with the model:
	```python
	from transformers import AutoTokenizer, AutoModelWithLMHead

	tokenizer = AutoTokenizer.from_pretrained("scottastrong/DialogGPT-medium-Scott")
	model = AutoModelWithLMHead.from_pretrained("scottastrong/DialogGPT-medium-Scott")
	# Let's chat for 4 lines
	for step in range(4):
	# 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')
	# print(new_user_input_ids)
	# 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=200,
	pad_token_id=tokenizer.eos_token_id,
	no_repeat_ngram_size=3,
	do_sample=True,
	top_k=100,
	top_p=0.7,
	temperature=0.8
	)

	# pretty print last ouput tokens from bot
	print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
	```