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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.
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