Instructions to use SPGT/LiveSafe-DialoGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SPGT/LiveSafe-DialoGPT with Transformers:

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

pipe = pipeline("text-generation", model="SPGT/LiveSafe-DialoGPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SPGT/LiveSafe-DialoGPT")
model = AutoModelForCausalLM.from_pretrained("SPGT/LiveSafe-DialoGPT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SPGT/LiveSafe-DialoGPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SPGT/LiveSafe-DialoGPT

SGLang

How to use SPGT/LiveSafe-DialoGPT 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 "SPGT/LiveSafe-DialoGPT" \
    --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": "SPGT/LiveSafe-DialoGPT",
		"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 "SPGT/LiveSafe-DialoGPT" \
        --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": "SPGT/LiveSafe-DialoGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SPGT/LiveSafe-DialoGPT with Docker Model Runner:
```
docker model run hf.co/SPGT/LiveSafe-DialoGPT
```

SPGT commited on Oct 19, 2021

Commit

c9dd8cf

1 Parent(s): 6d06789

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@@ -4,35 +4,3 @@ tags:
 license: mit
 ---
 ## 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.
-The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
-The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
-* Multi-turn generation examples from an interactive environment:
-|Role | Response |
-|---------|--------|
-|User | Does money buy happiness? |
-| Bot |  Depends how much money you spend on it .|
-|User | What is the best way to buy happiness ? |
-| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
-|User |This is so difficult ! |
-| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
-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 AutoModelForCausalLM, AutoTokenizer
-import torch
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
-model = AutoModelForCausalLM.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)))
-```

 license: mit
 ---
 ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)