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

Libraries

How to use skouras/DialoGPT-small-swda with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skouras/DialoGPT-small-swda")
model = AutoModelForCausalLM.from_pretrained("skouras/DialoGPT-small-swda")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use skouras/DialoGPT-small-swda with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/skouras/DialoGPT-small-swda

SGLang

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

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

DialoGPT-small finetuned in the Switchboard Dialogue Act (SwDa) Corpus. The repository with additionally pre-processed SwDa dialogues of concatenated utterances per speaker, 80/10/10 train/val/test split and metadata, is a fork of the Nathan Duran's repository. For finetuning the train_dialogpt.ipynb notebook from Nathan Cooper's Tutorial was used to finetune the model with slight modifications in Google Collab.

History dialogue context = 5. Number of utterances: 80704 (train set), 9749 (test set), 9616 (val set). Checkpoint-84000 after fine-tuning for 2 epochs with batch size 2.

Evaluation perplexity in SwDa from 635.6993 (pre-trainded model) to 18.1693 (fine-tuned model).

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