Instructions to use liam168/chat-DialoGPT-small-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liam168/chat-DialoGPT-small-en with Transformers:

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

pipe = pipeline("text-generation", model="liam168/chat-DialoGPT-small-en")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("liam168/chat-DialoGPT-small-en")
model = AutoModelForCausalLM.from_pretrained("liam168/chat-DialoGPT-small-en")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use liam168/chat-DialoGPT-small-en with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liam168/chat-DialoGPT-small-en"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liam168/chat-DialoGPT-small-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/liam168/chat-DialoGPT-small-en

SGLang

How to use liam168/chat-DialoGPT-small-en 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 "liam168/chat-DialoGPT-small-en" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liam168/chat-DialoGPT-small-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "liam168/chat-DialoGPT-small-en" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liam168/chat-DialoGPT-small-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use liam168/chat-DialoGPT-small-en with Docker Model Runner:
```
docker model run hf.co/liam168/chat-DialoGPT-small-en
```

liam168/chat-DialoGPT-small-en

Model description

用英文聊天数据训练的模型；

How to use

Now we are ready to try out how the model works as a chatting partner!

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

mode_name = 'liam168/chat-DialoGPT-small-en'
tokenizer = AutoTokenizer.from_pretrained(mode_name)
model = AutoModelForCausalLM.from_pretrained(mode_name)

# 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("Answer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

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