Instructions to use VextLabsinc/juwel-sapphire with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VextLabsinc/juwel-sapphire with Transformers:

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

pipe = pipeline("image-text-to-text", model="VextLabsinc/juwel-sapphire")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("VextLabsinc/juwel-sapphire")
model = AutoModelForMultimodalLM.from_pretrained("VextLabsinc/juwel-sapphire", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VextLabsinc/juwel-sapphire with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VextLabsinc/juwel-sapphire"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VextLabsinc/juwel-sapphire",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/VextLabsinc/juwel-sapphire

SGLang

How to use VextLabsinc/juwel-sapphire 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 "VextLabsinc/juwel-sapphire" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VextLabsinc/juwel-sapphire",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "VextLabsinc/juwel-sapphire" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VextLabsinc/juwel-sapphire",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use VextLabsinc/juwel-sapphire with Docker Model Runner:
```
docker model run hf.co/VextLabsinc/juwel-sapphire
```

JUWEL Sapphire

Release status: MIRRORED_STRUCTURAL_PASS. Every mirrored object matched the pinned public-R2 inventory, was SHA-256 hashed, and passed Hugging Face readback. Safetensors shards also matched the published index at header/tensor/offset level.

This repository contains public model weights mirrored from Vext Labs' already-public Cloudflare R2 release at https://pub-a6ae0476e46849f98f1746a61dc4c106.r2.dev/juwel-sapphire. It is not JUWEL's own-weights flagship Theta, and this card makes no SOTA, production-safety, or benchmark claim.

Artifact-derived specification

Architecture: Qwen3VLForConditionalGeneration
Geometry: 72,365,256,944 parameters; BF16; 144 text layers; hidden size 5,120; FFN 25,600; 64 attention heads / 8 KV heads; 262,144-token configured context; vocabulary 151,936; 27-layer vision tower, hidden size 1,152, patch size 16
Package: 30 safetensors shards; 1,938 indexed tensors; 144,730,513,888 tensor bytes
Lineage: Qwen3-VL architecture family. The recovered release metadata does not bind an exact upstream revision; no endorsement by Qwen is implied.

The exact source inventory and file hashes are recorded in mirror-receipts/4a500af65230058831c68cd323b836d3305e8dbbc3257d9f6b8e1d2cdbd42a2a.json.

Loading

from transformers import AutoModelForImageTextToText, AutoProcessor

repo = "VextLabsinc/juwel-sapphire"
model = AutoModelForImageTextToText.from_pretrained(repo, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(repo)

These are large BF16 artifacts. Use hardware and sharding appropriate to the package size.

Verification and limitations

The release receipt proves source inventory binding, full-file SHA-256, safetensors structural consistency, and destination byte readback. It does not prove full GPU inference, output quality, training-data provenance, safety behavior, or production fitness. Capability evaluation is pending. Review the exact configuration and receipt before use.

License

Apache License 2.0. The complete license text is in LICENSE. “Qwen” describes architecture lineage only and does not imply upstream endorsement. This repository includes no additional field-of-use restriction; Apache-2.0 governs the mirrored files. This is an Apache-licensed open-weight release, not a claim that the package satisfies the OSI Open Source AI Definition, which also considers training-data information and modification materials.

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Safetensors

Model size

72B params

Tensor type

BF16