juwel-emerald

JUWEL Emerald

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-emerald. 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: approximately 33,357,390,064 BF16 parameter values; 64 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: 14 safetensors shards; 1,058 indexed tensors; 66,714,780,128 tensor bytes
• Lineage: Qwen3-VL architecture family. Published geometry is 64 text layers; this artifact does not substantiate an additive-layer claim. No endorsement by Qwen is implied.

The exact source inventory and file hashes are recorded in mirror-receipts/06c952c569285870b811989b794b9766493e280fb77fbcb957fc4e5fcf25403a.json.

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from transformers import AutoModelForImageTextToText, AutoProcessor

repo = "VextLabsinc/juwel-emerald"
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.