Instructions to use morenolq/BART-IT-LSG-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use morenolq/BART-IT-LSG-4096 with Transformers:

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

pipe = pipeline("text-generation", model="morenolq/BART-IT-LSG-4096", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("morenolq/BART-IT-LSG-4096", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/BART-IT-LSG-4096", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use morenolq/BART-IT-LSG-4096 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "morenolq/BART-IT-LSG-4096"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "morenolq/BART-IT-LSG-4096",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/morenolq/BART-IT-LSG-4096

SGLang

How to use morenolq/BART-IT-LSG-4096 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 "morenolq/BART-IT-LSG-4096" \
    --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": "morenolq/BART-IT-LSG-4096",
		"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 "morenolq/BART-IT-LSG-4096" \
        --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": "morenolq/BART-IT-LSG-4096",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use morenolq/BART-IT-LSG-4096 with Docker Model Runner:
```
docker model run hf.co/morenolq/BART-IT-LSG-4096
```

morenolq commited on Feb 26, 2025

Commit

96332cc

verified ·

1 Parent(s): 102e609

Update README.md

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Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -66,7 +66,7 @@ They build upon **BART-IT** ([`morenolq/bart-it`](https://huggingface.co/morenol
 from transformers import BartForConditionalGeneration, AutoTokenizer
 # Load tokenizer and model
-model_name = "morenolq/LEGIT-BART"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
@@ -74,10 +74,10 @@ model = BartForConditionalGeneration.from_pretrained(model_name)
 input_text = "<mask> 1234: Il contratto si intende concluso quando..."
 inputs = tokenizer(input_text, return_tensors="pt", max_length=4096, truncation=True)
-# Generate summary
-summary_ids = model.generate(inputs.input_ids, max_length=150, num_beams=4, early_stopping=True)
-summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-print("📝 Summary:", summary)
 ```
 ---

 from transformers import BartForConditionalGeneration, AutoTokenizer
 # Load tokenizer and model
+model_name = "morenolq/BART-IT-LSG-4096"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 input_text = "<mask> 1234: Il contratto si intende concluso quando..."
 inputs = tokenizer(input_text, return_tensors="pt", max_length=4096, truncation=True)
+# Pre-trained model fill the mask
+output_ids = model.generate(inputs.input_ids, max_length=150, num_beams=4, early_stopping=True)
+output_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+print("📝:", output_text)
 ```
 ---