Instructions to use readerbench/RoSummary-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use readerbench/RoSummary-base with Transformers:

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

pipe = pipeline("text-generation", model="readerbench/RoSummary-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("readerbench/RoSummary-base")
model = AutoModelForCausalLM.from_pretrained("readerbench/RoSummary-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use readerbench/RoSummary-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/readerbench/RoSummary-base

SGLang

How to use readerbench/RoSummary-base 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 "readerbench/RoSummary-base" \
    --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": "readerbench/RoSummary-base",
		"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 "readerbench/RoSummary-base" \
        --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": "readerbench/RoSummary-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use readerbench/RoSummary-base with Docker Model Runner:
```
docker model run hf.co/readerbench/RoSummary-base
```

RoSummary-base / README.md

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	---
	datasets: readerbench/AlephNews
	---
	Model card for RoSummary-base

	---
	language:
	- ro
	---

	# RoSummary

	This is a version of the RoGPT2 model trained on the [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews) dataset for the summarization task. There are 3 trained versions, they are available on the HuggingFace Hub:
	* [base](https://huggingface.co/readerbench/RoSummary-base)
	* [medium](https://huggingface.co/readerbench/RoSummary-medium)
	* [large](https://huggingface.co/readerbench/RoSummary-large)


	## Evaluation on [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews)


	\| Model \| Decode Method \| \| BERTScore \| \| \| ROUGE \| \|
	\|:------:\|:--------------:\|:---------:\|:---------:\|:--------:\|:--------:\|:--------:\|:--------:\|
	\| \| \| Precision \| Recall \| F1-Score \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \|
	\| \| Greedy \| 0.7335 \| 0.7399 \| 0.7358 \| 0.3360 \| 0.1862 \| 0.3333 \|
	\| Base \| Beam Search \| 0.7354 \| 0.7468 \| 0.7404 \| 0.3480 \| 0.1991 \| 0.3416 \|
	\| \| Top-p Sampling \| 0.7296 \| 0.7299 \| 0.7292 \| 0.3058 \| 0.1452 \| 0.2951 \|
	\| \| Greedy \| 0.7378 \| 0.7401 \| 0.7380 \| 0.3422 \| 0.1922 \| 0.3394 \|
	\| Medium \| Beam Search \| 0.7390 \| 0.7493\|0.7434\|0.3546\|0.2061\|0.3467\|
	\| \| Top-p Sampling \| 0.7315 \| 0.7285 \| 0.7294 \| 0.3042 \| 0.1400 \| 0.2921 \|
	\| \| Greedy \| 0.7376 \| 0.7424 \| 0.7391 \| 0.3414 \| 0.1895 \| 0.3355 \|
	\| Large \| Beam Search \| 0.7394\| 0.7470 \| 0.7424 \| 0.3492 \| 0.1995 \| 0.3384 \|
	\| \| Top-p Sampling \| 0.7311 \| 0.7301 \| 0.7299 \| 0.3051 \| 0.1418 \| 0.2931 \|


	## Acknowledgments

	---

	Research supported with [Cloud TPUs](https://cloud.google.com/tpu/) from Google's [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc)