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 / LICENSE.txt

AbteeXAILabs

feat: initial LumynaX scaffold (card v6 + quickstart + manifest + Modelfile + Space scaffold)

cc82b99 verified about 1 month ago

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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.