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

Libraries

How to use crrodrvi/modelo_simplificacion_bart with Transformers:

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

pipe = pipeline("text-generation", model="crrodrvi/modelo_simplificacion_bart")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("crrodrvi/modelo_simplificacion_bart")
model = AutoModelForSeq2SeqLM.from_pretrained("crrodrvi/modelo_simplificacion_bart")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use crrodrvi/modelo_simplificacion_bart with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/crrodrvi/modelo_simplificacion_bart

SGLang

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

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

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

modelo_simplificacion_bart

This model is a fine-tuned version of facebook/mbart-large-50 on the lectura_dificl_facil dataset. It achieves the following results on the evaluation set:

Loss: 2.9828
Bleu: 9.1817

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5.6e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	109	3.1239	8.7241	29.2727
No log	2.0	218	3.0680	7.3130	23.9432
No log	3.0	327	2.9828	9.1817	28.5000

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for crrodrvi/modelo_simplificacion_bart

Base model

facebook/mbart-large-50

Finetuned

(351)

this model

crrodrvi
/

modelo_simplificacion_bart

modelo_simplificacion_bart

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for crrodrvi/modelo_simplificacion_bart

Dataset used to train crrodrvi/modelo_simplificacion_bart

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