Instructions to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final")

Transformers

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with Transformers:

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

pipe = pipeline("text-generation", model="umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final

SGLang

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final 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 "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final" \
    --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": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"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 "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final" \
        --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": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with Docker Model Runner:
```
docker model run hf.co/umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final
```

Dart66 commited on Feb 9

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@@ -97,8 +97,19 @@ paraphrased variants.
 - Datasets: 4.5.0
 - Tokenizers: 0.22.2
-## Citations
-author = {Sharon Calcina},
-year   = 2026,
-howpublished = {huggingface repo}

 - Datasets: 4.5.0
 - Tokenizers: 0.22.2
+## 📖 Citations
+If you use TRL, please cite:
+```bibtex
+@misc{vonwerra2022trl,
+  title        = {{TRL: Transformer Reinforcement Learning}},
+  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall
+                  and Edward Beeching and Tristan Thrush and Nathan Lambert
+                  and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+  year         = 2022,
+  journal      = {GitHub repository},
+  publisher    = {GitHub},
+  howpublished = {\url{https://github.com/huggingface/trl}}
+}