Instructions to use ipst/Qwen2.5-0.5B-Instruct-SLDS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ipst/Qwen2.5-0.5B-Instruct-SLDS with Transformers:

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

pipe = pipeline("text-generation", model="ipst/Qwen2.5-0.5B-Instruct-SLDS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ipst/Qwen2.5-0.5B-Instruct-SLDS")
model = AutoModelForCausalLM.from_pretrained("ipst/Qwen2.5-0.5B-Instruct-SLDS", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ipst/Qwen2.5-0.5B-Instruct-SLDS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ipst/Qwen2.5-0.5B-Instruct-SLDS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ipst/Qwen2.5-0.5B-Instruct-SLDS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ipst/Qwen2.5-0.5B-Instruct-SLDS

SGLang

How to use ipst/Qwen2.5-0.5B-Instruct-SLDS 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 "ipst/Qwen2.5-0.5B-Instruct-SLDS" \
    --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": "ipst/Qwen2.5-0.5B-Instruct-SLDS",
		"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 "ipst/Qwen2.5-0.5B-Instruct-SLDS" \
        --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": "ipst/Qwen2.5-0.5B-Instruct-SLDS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ipst/Qwen2.5-0.5B-Instruct-SLDS with Docker Model Runner:
```
docker model run hf.co/ipst/Qwen2.5-0.5B-Instruct-SLDS
```

Rolshoven commited on Nov 8, 2025

Commit

23b659b

verified ·

1 Parent(s): f3f6a0b

Updated citation

Browse files

Files changed (1) hide show

README.md +21 -8

README.md CHANGED Viewed

@@ -120,13 +120,26 @@ On the SLDS test set (2023–2024):
 If you use this model, please cite the dataset paper:
 ```bibtex
-@article{rolshoven2025slds,
-      title={Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland},
-      author={Luca Rolshoven and Vishvaksenan Rasiah and Srinanda Brügger Bose and Sarah Hostettler and Lara Burkhalter and Matthias Stürmer and Joel Niklaus},
-      year={2025},
-      eprint={2410.13456},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2410.13456},
 }
 ```

 If you use this model, please cite the dataset paper:
 ```bibtex
+@inproceedings{rolshoven-etal-2025-unlocking,
+    title = "Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in {S}witzerland",
+    author = {Rolshoven, Luca  and
+      Rasiah, Vishvaksenan  and
+      Bose, Srinanda Br{\"u}gger  and
+      Hostettler, Sarah  and
+      Burkhalter, Lara  and
+      St{\"u}rmer, Matthias  and
+      Niklaus, Joel},
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.findings-emnlp.832/",
+    pages = "15382--15411",
+    ISBN = "979-8-89176-335-7",
 }
 ```