| --- |
| license: apache-2.0 |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - juwel |
| - juwel-emerald |
| - vext-base |
| - vision-language |
| - cip |
| --- |
| |
| # JUWEL Emerald |
|
|
| > **JUWEL Emerald** (internal lineage: Vext-Base-v9) — the efficient, general-purpose Vext Labs |
| > base. Emerald is the May birthstone: evergreen. ~32B, vision-capable, the base most of the GEM |
| > specialist adapters are trained against. Released under Apache-2.0 as part of the JUWEL open |
| > archive. |
|
|
| ## At a glance |
|
|
| | Field | Value | |
| |---|---| |
| | Public name | JUWEL Emerald | |
| | HF repo | `VextLabsinc/juwel-emerald` | |
| | Internal lineage | Theron-Base v9 (Qwen3-VL-32B + CIP additive) | |
| | Parameters | ~32B | |
| | Precision | BF16 (no quantization) | |
| | Source | 14 safetensors shards, ~67 GB | |
| | Release license | Apache-2.0 | |
| | Hardware floor | 1x H100 80GB (BF16) | |
|
|
| ## What this is, honestly |
|
|
| JUWEL Emerald is the dense, efficient general base in the Vext lineage — Qwen3-VL-32B with a CIP |
| additive step. It is the base the GEM specialist fleet loads on top of. It is released as open |
| weights because it is a strong, useful base and because the community feedback loop on it feeds |
| how we build forward. |
|
|
| ## Intended use |
|
|
| - General reasoning / language / vision baseline at ~32B. |
| - Base for the GEM specialist adapters (`VextLabsinc/gem-*`) and for your own domain fine-tunes. |
| - Self-hosted deployment without depending on Vext-hosted inference. |
|
|
| ## Out of scope |
|
|
| - Not a substitute for a licensed professional in any regulated domain. |
| - No deliberate refusal training — apply your own safety policy at the human-to-model boundary. |
| - Quantized inference if quality is the goal — trained/validated in BF16. |
|
|
| ## Architecture / training (outcome-level) |
|
|
| - Derived from Qwen3-VL-32B; capability added via CIP (organic upscale + LoRA on new layers, |
| then merged). We describe CIP at the outcome level only; the recipe is proprietary. |
| - No teacher distillation — training examples are curated primary sources. Raw corpus stays |
| proprietary; a data card describing source families ships with the release. |
|
|
| ## Evaluation |
|
|
| - **Status:** pending — numbers published only when reproducible on our stack (named harness, |
| date, method), per our benchmark-audit-trail discipline. Do not treat internal rubrics as |
| public leaderboards. Headline lineage note: v9 beat the retired v8 by a wide margin on |
| GSM8K / MMLU-Pro at one-third the footprint (audit trail: https://vextlabs.ai/transparency). |
|
|
| ## Weights — download (Cloudflare R2, public, BF16) |
|
|
| > Weights live on Cloudflare R2, not inside this HF repo. Download them locally, then load from the local dir. |
|
|
| ```bash |
| BASE=https://pub-a6ae0476e46849f98f1746a61dc4c106.r2.dev/juwel-emerald |
| mkdir -p juwel-emerald && cd juwel-emerald |
| for f in config.json model.safetensors.index.json tokenizer.json tokenizer_config.json generation_config.json; do curl -sO $BASE/$f; done |
| for i in $(seq -w 1 14); do curl -O $BASE/model-000$i-of-00014.safetensors; done |
| ``` |
|
|
| ## How to load |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| model = AutoModelForCausalLM.from_pretrained("./juwel-emerald", torch_dtype=torch.bfloat16, device_map="auto") |
| tokenizer = AutoTokenizer.from_pretrained("./juwel-emerald") |
| ``` |
|
|
| Load a GEM specialist on top (download its adapter from R2 first — see the GEM's card — then): |
|
|
| ```python |
| from peft import PeftModel |
| model = PeftModel.from_pretrained(model, "./gem-ruby") # code specialist, downloaded from R2 |
| ``` |
|
|
| ## License / attribution |
|
|
| Derived from **Qwen3-VL-32B** (Alibaba Cloud). Preserve the base license + `NOTICE`. JUWEL Emerald |
| is released under **Apache-2.0**. Do not imply Alibaba/Qwen endorsement. Contact: info@vextlabs.ai. |
|
|
