Instructions to use Batsy24/DialoGPT-small-Twilight_EdBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Batsy24/DialoGPT-small-Twilight_EdBot with Transformers:

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

pipe = pipeline("text-generation", model="Batsy24/DialoGPT-small-Twilight_EdBot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Batsy24/DialoGPT-small-Twilight_EdBot")
model = AutoModelForCausalLM.from_pretrained("Batsy24/DialoGPT-small-Twilight_EdBot")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Batsy24/DialoGPT-small-Twilight_EdBot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Batsy24/DialoGPT-small-Twilight_EdBot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Batsy24/DialoGPT-small-Twilight_EdBot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Batsy24/DialoGPT-small-Twilight_EdBot

SGLang

How to use Batsy24/DialoGPT-small-Twilight_EdBot 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 "Batsy24/DialoGPT-small-Twilight_EdBot" \
    --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": "Batsy24/DialoGPT-small-Twilight_EdBot",
		"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 "Batsy24/DialoGPT-small-Twilight_EdBot" \
        --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": "Batsy24/DialoGPT-small-Twilight_EdBot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Batsy24/DialoGPT-small-Twilight_EdBot with Docker Model Runner:
```
docker model run hf.co/Batsy24/DialoGPT-small-Twilight_EdBot
```

DialoGPT-small-Twilight_EdBot / model.safetensors

SFconvertbot

Adding `safetensors` variant of this model

bd0d2a0 verified over 1 year ago

download

history blame

510 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5f3c7415ee75e652c446e8518f6774946b06d27a4589a59708006c0dddc2b90f
Size of remote file:: 510 MB
SHA256:: 31130b23202a41f9c7c5fec78fe99498c0c3f0c75ae43711b58cf9a2f4dae138

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.