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license: apache-2.0
base_model: VextLabsinc/juwel-beryl
library_name: peft
pipeline_tag: text-generation
tags:
- juwel
- gem
- gem-ruby
- code
- lora
- vext
---
> **Both halves of this model are on the Hub.** The adapter weights live in this repo and the required base is [`VextLabsinc/juwel-beryl`](https://huggingface.co/VextLabsinc/juwel-beryl) (80-layer, BF16). Load the base with `AutoModelForImageTextToText`, then apply this adapter.
# GEM Ruby · code specialist
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Template for every public open-weight release.
Fill on the GPU pod before huggingface-cli upload.
Never claim first/all weights / AGI / never-forgets / cheapest-ever.
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**Model id:** `VextLabsinc/gem-ruby`
**Org:** Vext Labs, Inc.
**License:** Apache-2.0 (see `LICENSE`) — **AS IS, no warranty**
**Library:** `peft` <!-- peft | transformers -->
**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=64, alpha=128) — 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-ruby")
```
## Intended use
- Research, education, and **authorized** professional workflows in the **code** 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. <!-- e.g. internal rubric 2026-04-02 | pending | public harness -->
- **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/code/` (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
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## Weights — download
- **Adapter (this model):** hosted in this HF repo — `adapter_model.safetensors` + `adapter_config.json`. Loads with `PeftModel.from_pretrained(base, "VextLabsinc/gem-ruby")`. (R2 mirror: https://pub-a6ae0476e46849f98f1746a61dc4c106.r2.dev/gem-ruby/)
- **Base (`juwel-beryl`, 80L, BF16):** on the Hub at https://huggingface.co/VextLabsinc/juwel-beryl — loads directly by repo id, no manual download needed.
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