Instructions to use anezatra/gpt2-samsum-124M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anezatra/gpt2-samsum-124M with Transformers:

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

pipe = pipeline("text-generation", model="anezatra/gpt2-samsum-124M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anezatra/gpt2-samsum-124M")
model = AutoModelForCausalLM.from_pretrained("anezatra/gpt2-samsum-124M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anezatra/gpt2-samsum-124M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anezatra/gpt2-samsum-124M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anezatra/gpt2-samsum-124M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anezatra/gpt2-samsum-124M

SGLang

How to use anezatra/gpt2-samsum-124M 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 "anezatra/gpt2-samsum-124M" \
    --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": "anezatra/gpt2-samsum-124M",
		"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 "anezatra/gpt2-samsum-124M" \
        --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": "anezatra/gpt2-samsum-124M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anezatra/gpt2-samsum-124M with Docker Model Runner:
```
docker model run hf.co/anezatra/gpt2-samsum-124M
```

OpenAI GPT-2 Samsum

Model description

This model has been trained with the SAMSum dataset. The SAMSum dataset contains approximately 16,000 conversational dialogues accompanied by summaries. These conversations were created and written by linguists proficient in fluent English. Linguists were instructed to create conversations that reflect the ratio of topics found in real-life journalistic conversations similar to their daily written conversations. The style and tone vary; conversations can be informal, semi-formal, or formal, and may include slang terms, expressions, and spelling errors. Subsequently, the conversations were annotated with summaries. The summaries are expected to be concise summaries of what people were talking about during the conversation, written in the third person. The SAMSum dataset was prepared by the Samsung Research Institute Poland and is distributed for research purposes.

Training

This GPT-2 model is rated for an average of 1 hour with an L4 GPU.

Training Results

Authors

Developed by: Anezatra
Model type: GPT2
Contacts: https://github.com/anezatra

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32