Instructions to use AbteeXAILab/lumynax-translate-nllb-200-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbteeXAILab/lumynax-translate-nllb-200-3b with Transformers:

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

pipe = pipeline("text-generation", model="AbteeXAILab/lumynax-translate-nllb-200-3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AbteeXAILab/lumynax-translate-nllb-200-3b")
model = AutoModelForMultimodalLM.from_pretrained("AbteeXAILab/lumynax-translate-nllb-200-3b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbteeXAILab/lumynax-translate-nllb-200-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbteeXAILab/lumynax-translate-nllb-200-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbteeXAILab/lumynax-translate-nllb-200-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-translate-nllb-200-3b

SGLang

How to use AbteeXAILab/lumynax-translate-nllb-200-3b 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 "AbteeXAILab/lumynax-translate-nllb-200-3b" \
    --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": "AbteeXAILab/lumynax-translate-nllb-200-3b",
		"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 "AbteeXAILab/lumynax-translate-nllb-200-3b" \
        --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": "AbteeXAILab/lumynax-translate-nllb-200-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AbteeXAILab/lumynax-translate-nllb-200-3b with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-translate-nllb-200-3b
```

lumynax-translate-nllb-200-3b

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LumynaX release package — distribution scaffold

This Hugging Face repository contains the LumynaX release scaffold for the
upstream model identified in `release_export_manifest.json`. The model weights
themselves remain governed by the upstream licence at `metadata.upstream_repo`
and are fetched by the runtime via Hugging Face Hub when you execute
`quickstart.py`.

The scaffold (configs, manifest, quickstart, ollama Modelfile, Space app) is
released under the MIT licence by AbteeX AI Labs (Aotearoa New Zealand). Your
use of the *upstream weights* is governed by the *upstream licence*; this file
does not relicense them.

Provenance, residency and audit obligations are defined in
`release_export_manifest.json` and the model card.