Both halves of this model are on the Hub. The adapter weights live in this repo and the required base is VextLabsinc/juwel-beryl (80-layer, BF16). Load the base with AutoModelForImageTextToText, then apply this adapter.

GEM Turquoise · reconciler specialist

Model id: VextLabsinc/gem-turquoise
Org: Vext Labs, Inc.
License: Apache-2.0 (see LICENSE) — AS IS, no warranty
Library: peft
Pipeline: text-generation

Base model

Required base: VextLabsinc/juwel-beryl
Architecture notes: Base = JUWEL Beryl (80-layer, hidden 5120, 64 heads, intermediate 25600; internal lineage theron-base-v9 + one CIP rung). This PEFT LoRA is native to that 80-layer geometry (layers_to_transform 0-79, r=32, alpha=64) — it is not padded and not drop-in on any other JUWEL base. Base architecture is Qwen3VLForConditionalGeneration (image-text-to-text), so load it with AutoModelForImageTextToText, not AutoModelForCausalLM.
Base license / attribution: see NOTICE

If this is a LoRA / PEFT adapter, load base first, then:

python

from transformers import AutoModelForImageTextToText
from peft import PeftModel
base = AutoModelForImageTextToText.from_pretrained("VextLabsinc/juwel-beryl", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "VextLabsinc/gem-turquoise")

Intended use

Research, education, and authorized professional workflows in the reconciler domain.
Integration into systems where a human remains responsible for outcomes.

Out of scope / prohibited uses

You may not use this model for:

Unauthorized access to computer systems, networks, or accounts
Development or deployment of malware, ransomware, or fraud
Child sexual abuse material or any illegal content
Weapons development or violent crime
Any use that violates applicable law or third-party rights
For security-related models: testing only on systems you own or have explicit written permission to assess

Vext Labs does not endorse misuse. Publishing weights is not permission to break the law.

Limitations

Outputs can be wrong, biased, or unsafe if misused.
Not a substitute for licensed professionals (medical, legal, financial, security).
Not guaranteed to refuse harmful requests; apply your own filters and policies.
Domain specialist adapter; quality varies by prompt.

Training data (summary)

Domain specialist continued training / LoRA on Theron lineage; see lab training docs. No customer confidential data intended.
No customer confidential data is intentionally included in this release package.
Downstream fine-tunes by third parties are not controlled by Vext Labs.

Evaluation

Status: PENDING — no reproducible benchmark published yet. Evals run on our OWN BF16 weights on a GPU pod (never a hosted API), then posted here with a full audit trail (raw responses, test cases, timestamps, model version). We publish no score we cannot reproduce on our own stack.
Numbers: None yet — honest placeholder, not a hidden result. Benchmarks were blocked until 2026-07-28 by a base-identity error: these cards named a 144-layer base for an 80-layer adapter, so the adapter was not loadable as trained. The base identity is now corrected and evaluation is under way.
Do not treat internal rubrics as standardized public leaderboards (MMLU/HELM/etc.) unless re-run under a named public harness.

Files / integrity

Weight files: see repository file list
Checksums: SHA256SUMS
R2 source (internal): s3://vext-theron-fleet/v10_loras_v9_padded/reconciler/ (not a public download URL)

Liability

These weights are provided AS IS under the LICENSE.
To the maximum extent permitted by law, Vext Labs, Inc. disclaims all warranties and is not liable for damages arising from use or misuse of this model.
You are solely responsible for compliance with law and for authorized use only.
See also USE_POLICY.md.

Contact

Product / lab: https://vextlabs.ai · https://juwel.ai
Disclosure / questions: info@vextlabs.ai

Weights — download

Adapter (this model): hosted in this HF repo — adapter_model.safetensors + adapter_config.json. Loads with PeftModel.from_pretrained(base, "VextLabsinc/gem-turquoise"). (R2 mirror: https://pub-a6ae0476e46849f98f1746a61dc4c106.r2.dev/gem-turquoise/)
Base (juwel-beryl, 80L, BF16): on the Hub at https://huggingface.co/VextLabsinc/juwel-beryl — loads directly by repo id, no manual download needed.