| --- |
| 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 |
|
|
| <!-- |
| 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. |
| --> |
|
|
| **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 |
|
|
|
|
| <!-- legal_tier: T1 --> |
| |
| ## 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. |
|
|
