Instructions to use KennethTM/gpt2-small-danish-review-response with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KennethTM/gpt2-small-danish-review-response with Transformers:

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

pipe = pipeline("text-generation", model="KennethTM/gpt2-small-danish-review-response")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish-review-response")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish-review-response")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KennethTM/gpt2-small-danish-review-response with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KennethTM/gpt2-small-danish-review-response"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KennethTM/gpt2-small-danish-review-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KennethTM/gpt2-small-danish-review-response

SGLang

How to use KennethTM/gpt2-small-danish-review-response 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 "KennethTM/gpt2-small-danish-review-response" \
    --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": "KennethTM/gpt2-small-danish-review-response",
		"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 "KennethTM/gpt2-small-danish-review-response" \
        --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": "KennethTM/gpt2-small-danish-review-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KennethTM/gpt2-small-danish-review-response with Docker Model Runner:
```
docker model run hf.co/KennethTM/gpt2-small-danish-review-response
```

gpt2-small-danish-review-response / README.md

KennethTM

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metadata

language:
  - da
pipeline_tag: text-generation
widget:
  - text: |
      ### Bruger:
      Anders

      ### Anmeldelse:
      Umuligt at komme igennem på telefonen.

      ### Svar:
      Kære Anders

What is this?

A fine-tuned GPT-2 model (small version, 124 M parameters) for generating responses to customer reviews in Danish.

How to use

The model is based on the gpt2-small-danish model. Supervised fine-tuning is applied to adapt the model to generate responses to customer reviews in Danish. A prompting template is applied to the examples used to train (see the example below).

Test the model using the pipeline from the 🤗 Transformers library:

from transformers import pipeline

generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish-review-response")

def prompt_template(user, review):
    return f"### Bruger:\n{user}\n\n### Anmeldelse:\n{review}\n\n### Svar:\nKære {user}\n"

prompt = prompt_template(user = "Anders", review = "Umuligt at komme igennem på telefonen.")

text = generator(prompt)

print(text[0]["generated_text"])

Or load it using the Auto* classes:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish-review-response")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish-review-response")

Notes

The model may get the sentiment of the review wrong resulting in a mismatch between the review and response. The model would probably benefit from sentiment tuning.