JUWEL Garnet

JUWEL Garnet (internal lineage: Vext-Base-v8) — the eighth-generation Vext Labs base model and the origin stone of the open archive. Garnet is the January birthstone: the beginning. Released under Apache 2.0 after being superseded internally by JUWEL Emerald (Vext-Base-v9).

Built better. Priced fairly. And what we used to ship, the world can ship too.

Vext-Base-v8 is the first open-source release in the Vext Labs OSS archive program. It is the BF16 base model that powered the Theron entity from 2026-04-04 through 2026-05-13, formerly named "Theron-Base v8" while it was the live flagship. It is published here under the public "Vext-*" name so that the Theron brand stays attached to the live flagship and downstream forks can't muddy the name.

At a glance

Field	Value
Public name	JUWEL Garnet
HF repo	`VextLabsinc/juwel-garnet`
Internal lineage	Theron-Base v8 (Vext-Base-v8)
Architecture	Transformer (Qwen3 lineage, organically upscaled)
Parameters	~78B post-merge
Precision	BF16 (no quantization)
Context window	131K (YaRN extended)
Attention	GQA 64:8 + FlashAttention-3
Live as flagship	2026-04-04 — 2026-05-13
Superseded by	Vext-Base-v9 (Qwen3-VL-32B + CIP additive)
Release license	Apache-2.0
Release date (target)	2026-06-12
Hardware floor	1× H100 80GB for inference (BF16 with offload), 2× H100 80GB recommended

What this is, honestly

This is a retired Vext flagship. The current production system uses Vext-Base-v9 and a Composition layer of 15 cluster LoRAs that delivers materially better numbers on every domain we benchmark (headline: +51 GSM8K, +53 MMLU-Pro vs v8 measured on the same harness — see the audit trail at https://vextlabs.ai/transparency).

We release v8 anyway because:

v8 is still a strong model — at its retirement it was within striking distance of contemporary frontier-class bases on general benchmarks, and remains a useful research artifact.
The world deserves access. Frontier labs keep their last three generations sealed; we keep our last one in your hands.
Feedback loop. When the open community uses, breaks, fine-tunes, and benchmarks v8, that signal feeds back into how we build v10+.

Intended use

Research baselines for transformer scaling / composition / CIP papers
Fine-tuning starting point for domain LoRAs
Self-hosted deployment for organizations that want the Vext lineage without depending on Vext-hosted inference
Inputs to derivative open-source models (the Apache-2.0 license permits this with attribution)

Out of scope (we explicitly don't recommend these)

Replacement for the live Vext-Base-v9 + Composition stack — quality delta is significant, especially on reasoning and math
Production deployment without your own evaluation harness — every model has corner cases the headline numbers don't capture
Distillation as a teacher — we don't distill on principle (see feedback_no_distillation.md) and we don't recommend others do either
Quantized inference if quality is the goal — v8 was trained and validated in BF16; INT4/INT8 hits the long tail hard

Training

Recipe: V6+ canonical (4× H100 + DDP + local SSD + device_map per rank). Documented at scripts/theron/train_theron_base_v8.py in the Vext repo.
No teacher distillation. Every training example is a curated primary source (real documents, validated traces, verified answers). The raw corpus stays proprietary (we never published an LLM-generated Q&A pool, and we don't ship one here); a data card describing the source families + licensing is included so downstream users can reproduce the recipe shape.
Organic upscale + CIP for capability injection: instead of retraining existing weights, we add +16 layers and LoRA-train only the new layers, then merge. v8 was built this way over v7.

Eval

Honest delta vs the successor Vext-Base-v9:

Benchmark	Vext-Base-v8	Vext-Base-v9	Δ
GSM8K	(published in audit trail)	+51	v9 ahead
MMLU-Pro	(published in audit trail)	+53	v9 ahead
HumanEval	(published in audit trail)	small Δ	comparable

Full audit trail (raw responses, extracted code, test cases, errors, timestamps, model version) lives at https://vextlabs.ai/transparency per feedback_benchmark_audit_trail.md. Pass/fail only is unverifiable; we publish the trail so external auditors can reproduce.

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. (Garnet is a Qwen3-Next MoE base — the GEM LoRA adapters do NOT apply to it; it is a standalone base.)

BASE=https://pub-a6ae0476e46849f98f1746a61dc4c106.r2.dev/juwel-garnet
mkdir -p juwel-garnet && cd juwel-garnet
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 32); do curl -O $BASE/model-000$i-of-00032.safetensors; done

How to load

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "./juwel-garnet",
    torch_dtype=torch.bfloat16,   # do NOT quantize — see notes above
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("./juwel-garnet")

inputs = tokenizer("Explain how attention works in one paragraph.", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Hardware requirements

Setting	Floor	Recommended
BF16 inference with offload	1× H100 80GB + system RAM	2× H100 80GB
BF16 inference fully on GPU	2× H100 80GB	4× H100 80GB
LoRA fine-tune	4× H100 80GB DDP	8× H100 80GB DDP
Full fine-tune	8× H100 80GB	16× H100 80GB

vLLM and SGLang both load this checkpoint out of the box. Vext Labs serves the live successor on SGLang TurboLoRA — see https://vextlabs.ai/technology.

Known limitations

v8 inherits all known limitations of its base lineage (Qwen3) plus any specific quirks introduced by the CIP additive training step.
Performance below v9 on reasoning + math (see Eval table). For best results pair with a verifier (Vext ships Iris; OSS alternatives like Self-RAG, CoT-SC, MCTS-style search all compose cleanly).
No deliberate refusal training (see feedback_no_refusal_ever.md). Users are expected to apply their own safety policy at the human-to-model boundary; we don't ship one baked in.

License

Apache License 2.0. Use it. Fork it. Build on it. Attribution appreciated; not required for the weights themselves.

Citation

@misc{juwel-garnet,
  author       = {{Vext Labs}},
  title        = {{JUWEL Garnet: A Retired Flagship, Open}},
  year         = 2026,
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/VextLabsinc/juwel-garnet},
  note         = {Internal lineage Theron-Base v8 / Vext-Base-v8. Apache-2.0.},
}

Acknowledgements

Built on the shoulders of the Qwen3 lineage and the broader open-research community whose papers + datasets shaped every stage of training. The OSS archive program is a 30-day-after-supersession cadence (see https://vextlabs.ai/open-source) and every retired Vext base, LoRA, and Brain version follows v8 here.

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