Instructions to use HugoZeballos/nllb-esp-rap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HugoZeballos/nllb-esp-rap with Transformers:

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

pipe = pipeline("text-generation", model="HugoZeballos/nllb-esp-rap")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("HugoZeballos/nllb-esp-rap")
model = AutoModelForSeq2SeqLM.from_pretrained("HugoZeballos/nllb-esp-rap")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HugoZeballos/nllb-esp-rap with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HugoZeballos/nllb-esp-rap

SGLang

How to use HugoZeballos/nllb-esp-rap 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 "HugoZeballos/nllb-esp-rap" \
    --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": "HugoZeballos/nllb-esp-rap",
		"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 "HugoZeballos/nllb-esp-rap" \
        --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": "HugoZeballos/nllb-esp-rap",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HugoZeballos/nllb-esp-rap with Docker Model Runner:
```
docker model run hf.co/HugoZeballos/nllb-esp-rap
```

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

NLLB-esp-rap

This is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M

Intended uses & limitations

The first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture.

This model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set.

Finally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model.

How to use

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NLLB-esp-rap

Intended uses & limitations

How to use

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