Wildcat

Wildcat is d3-ultra squeezed to 3 bits, then repaired. It answers the same decision questions as vllm-sr/d3-ultra (pick one option, yes / no, rate on a scale) and returns a probability for every answer. It loads in about 13 GB of GPU memory and runs on a single 24 GB card (tested on an RTX 3090). d3-ultra in bf16 needs about 54 GB.

Base	d3-ultra (27B, Qwen3.5 family), Apache-2.0
Weights	400 text Linears in 3-bit HQQ (group 64), packed; everything else bf16 as in d3-ultra
Repair	Two LoRA adapters (rank 32, two training seeds) averaged, applied on top of the 3-bit weights
GPU memory	13.19 GiB loaded; runs on 24 GB cards
Download	13.6 GiB
License	Fastino Community License (see below). Built with Fastino GLiDE.

How it was made

1. Squeeze. d3-ultra's text-layer weights are quantized to 3 bits with HQQ (group size 64). The vision tower, embeddings, norms and the small in_proj_a / in_proj_b projections stay in bf16. The 3-bit codes are stored packed (a 2-bit plane plus a 1-bit plane); a small Triton kernel unpacks each layer just before its matmul, bit for bit equal to HQQ's own dequantize.
2. Repair. A LoRA is trained on top of the frozen 3-bit model to match the answer probabilities of three teachers: d3-ultra, Jeffan and Fastino GLiDE. The questions come only from public training splits of permissively licensed datasets, checked against the Decision Index for leaks. No Decision Index data was used for training.
3. Blend. Two runs with different seeds are averaged (each adapter at half strength).

Results

Decision Index 0.3 public index: 70.66 (complete run, all 140,620 requests, one RTX PRO 6000). d3-ultra's public index on the board is 71.57, so Wildcat is 0.9 points below the model it was made from, at a quarter of the memory. The board's own ranking uses its private tests, which we have not seen.

Area (public index, skill)	Wildcat	d3-ultra (board)
Knowledge & reasoning	58.9	62.3
Language understanding	73.7	72.4
Retrieval & classification	71.9	71.8
Tools & automation	87.2	87.8
Arts & human taste	60.7	63.5
Index	70.66	71.57

Where the 3-bit squeeze shows: multi-step reasoning and hard knowledge (MuSR, GPQA Diamond, CRUXEval, MMLU-Pro, GSM8K are 3 to 10 points lower). The repair LoRA gains on language tasks (iSarcasmEval +8.7, FinEntity +2.7).

Speed over the full public run on one RTX PRO 6000 (in-process, one request at a time): median 157 ms, p80 351 ms per request. The mean is 1,258 ms because about 1,000 very long ToolRet requests take minutes each; without ToolRet the mean is 438 ms. On 750 sampled rows timed the same way, Wildcat and d3-ultra were level (mean 818 vs 932 ms).

On cards under 40 GB the engine answers one question at a time (batch size 1) so that requests with many long questions fit; on cards over 72 GB it unpacks to bf16 and merges the LoRA for speed. Very long requests can fall back to a slower path that still answers them.

What was not tested

• Box and Track (image and video locations). d3-ultra supports them; Wildcat was only measured on choice, yes / no and score questions. Expect them to be weaker than d3-ultra's.
• AMD GPUs. The packed kernel is Triton and was only run on NVIDIA (CUDA).

Run it

Needs an NVIDIA GPU with 24 GB or more, and the packages in requirements.txt plus triton and accelerate.

hf download BoldingBuilds/Wildcat --local-dir wildcat

With the Decision Index kit (this is how the board runs it):

PYTHONPATH=wildcat python -m decision_index run --engine d3_q3:D3Q3Engine --option model=wildcat \
  --rows <rows.jsonl.gz> --out <run dir>

The LoRA in lora.safetensors attaches on load. Nothing else needs passing.

Files

File	What it is
q3.safetensors	the packed 3-bit Linears (<module>.lo / .hi / .scale / .zero)
model-rest.safetensors	every other backbone tensor, bf16
lora.safetensors	the repair LoRA, both seeds stacked and scaled (<module>.A / .B)
readout.safetensors, tokenizer, configs, d3_*.py	from d3-ultra, unchanged
d3_q3.py	the 3-bit engine and loader
MODEL_MANIFEST.json	SHA-256 of every file; checked on load

License

Wildcat learned from Fastino GLiDE's answers, so it is a Derivative Work under the Fastino Community License and ships under it. In short:

• Free to use, change and share. Keep this license with any copy.
• Commercial use: show "Built with Fastino GLiDE" in the product's docs, about page or UI.
• Companies with more than $10M yearly revenue using it in production need a separate agreement with Fastino (hello@fastino.ai). Evaluation, testing, benchmarking and research are not production.

d3-ultra's weights, readout and runtime are Apache-2.0 (LICENSE, NOTICE). Qwen's Apache-2.0 license is in LICENSES/Qwen3.8-27B-LICENSE.

Built with Fastino GLiDE.

Credits

• d3-ultra and its runtime: the vLLM Semantic Router team (vllm-sr/d3-ultra). Wildcat is their model, made smaller.
• Fastino GLiDE: teacher (fastino/GLiDE).
• Jeffan (tattrongvu): teacher.
• Qwen (Alibaba Cloud): the base model family.
• HQQ (Mobius Labs): the 3-bit quantizer.
• The Decision Index (multimodalart) for the benchmark and kit.
• The authors of every public dataset in the repair mix (below).

Repair data

36,559 questions, all from train splits of public datasets (or generated here), each checked against the Decision Index public rows for overlapping text; overlapping rows were dropped. Several sources are the train splits of benchmarks the Decision Index tests (for example BANKING77, CLINC150, GSM8K, WinoGrande, HellaSwag, When2Call, Amazon ESCI, iSarcasmEval, New Yorker captions), so expect Wildcat to do relatively better there.

• Intent and topic: CLINC150, BANKING77, DBpedia-14, LEDGAR
• Reading, inference and paraphrase: SQuAD v2, BoolQ, SNLI, WANLI, VitaminC, PAWS, STS-B, PubMedQA, claim_stance + Perspectrum
• Commonsense and science: HellaSwag, WinoGrande, PIQA, Social IQa, CommonsenseQA, Cosmos QA, ARC, OpenBookQA, QASC, RuleTaker
• Maths and knowledge: GSM8K, AQuA-RAT, MATH (hendrycks), MMLU (train), MedMCQA
• Ratings and taste: Feedback Collection, HelpSteer2, WMT DA, GoEmotions, Civil Comments, New Yorker Caption Contest, iSarcasmEval
• Tools and search: When2Call, Amazon ESCI
• Generated here (MIT): chord, CRUX-style code tracing, BBH-style, CLadder-style and SQuAD-based hallucination questions

Each source keeps its own licence (CC-BY, CC-BY-SA, Apache-2.0, MIT, CC0, AFL-3.0); no non-commercial data.