Instructions to use frhew/sigdial_ft_a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use frhew/sigdial_ft_a2 with Transformers:

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

pipe = pipeline("text-generation", model="frhew/sigdial_ft_a2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("frhew/sigdial_ft_a2")
model = AutoModelForCausalLM.from_pretrained("frhew/sigdial_ft_a2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use frhew/sigdial_ft_a2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frhew/sigdial_ft_a2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frhew/sigdial_ft_a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/frhew/sigdial_ft_a2

SGLang

How to use frhew/sigdial_ft_a2 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 "frhew/sigdial_ft_a2" \
    --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": "frhew/sigdial_ft_a2",
		"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 "frhew/sigdial_ft_a2" \
        --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": "frhew/sigdial_ft_a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use frhew/sigdial_ft_a2 with Docker Model Runner:
```
docker model run hf.co/frhew/sigdial_ft_a2
```

Model Card for Model ID

This model was used in our experiments in our paper: Elaborative Simplification for German-Language Texts. We have uploaded this model for transparency and replicability of our experiments. If however you are interested in German text simplification in general, we recommend our more recent model.

We fine-tuned meta-llama/Meta-Llama-3-8B-Instruct with a set of ca. 2000 newspaper articles which have been simplified by the Austrian Press Agency. This model was trained with the standard and the A2 level texts.

Model Details

Model Description

Developed by: Freya Hewett, Hadi Asghari
Model type: simplification model, text generation
Language(s) (NLP): German
License: Apache 2.0
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct

Model Sources

Repository: GermanElabSimplification
Paper: Elaborative Simplification for German-Language Texts

Uses

Direct Use

This model works best for simplifying German-language newspaper articles (news items, not commentaries or editorials). It may work for other types of texts.

Downstream Use

We have fine-tuned using only newspaper articles. We have not yet performed extensive out-of-domain testing, but believe that the model's capabilities could be improved by fine-tuning on more diverse data.

Bias, Risks, and Limitations

As with most text generation models, the model sometimes produces information that is incorrect.

Recommendations

Please check manually that your output text corresponds to the input text, as factual inconsistencies may have arisen.

How to Get Started with the Model

To load the model using transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("frhew/sigdial_ft_a2")
model = AutoModelForCausalLM.from_pretrained("frhew/sigdial_ft_a2", torch_dtype=torch.float16).to(device)

We used the following prompt at inference to test our model:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Du bist ein hilfreicher Assistent und hilfst dem User, Texte besser zu verstehen.<|eot_id|><|start_header_id|>user<|end_header_id|>
Kannst du bitte den folgenden Text zusammenfassen und sprachlich auf ein A2-Niveau in Deutsch vereinfachen? Schreibe maximal 5 Sätze.
{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Training Details

Training Data

A sample of the data used to train our model can be found here.

Training Hyperparameters

Evaluation

The right hand side shows the results of the manual evaluation, done on the outputs from each model for 35 texts. M.P. stands for meaning preservation, S for simplification, C for coherence, F for factuality; the score represents the percentage of yes answers. More details on the evaluation can be found in the paper. For all metrics, higher is better.

Model	Prompt	Test set	SARI	FRE	M.P.	S	C	F	Avg.
Baseline	Basic	A2	41.2	59.4	.89	.38	.96	.84	.77
FT-A2	Basic	A2	44.0	70.6	.49	.82	.56	.64	.63
Baseline	Basic	B1	42.3	56.8	.85	.4	.9	.9	.76
FT-B1	Basic	B1	42.4	60.0	.75	.55	.6	.75	.66

Summary

Citation

BibTeX:

@inproceedings{hewett-etal-2024-elaborative, title = "Elaborative Simplification for {G}erman-Language Texts", author = "Hewett, Freya and Asghari, Hadi and Stede, Manfred", editor = "Kawahara, Tatsuya and Demberg, Vera and Ultes, Stefan and Inoue, Koji and Mehri, Shikib and Howcroft, David and Komatani, Kazunori", booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue", month = sep, year = "2024", address = "Kyoto, Japan", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.sigdial-1.3", doi = "10.18653/v1/2024.sigdial-1.3", pages = "29--39"}

APA:

Freya Hewett, Hadi Asghari, and Manfred Stede. 2024. Elaborative Simplification for German-Language Texts. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 29–39, Kyoto, Japan. Association for Computational Linguistics.

Model Card Contact

frhew

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Safetensors

Model size

8B params

Tensor type

F32