d3-oQ4e

Model · Zero-shot classification

matilda-jev-v1

Maincode

Matilda-Jev is Maincode's one-pass decision model. It scores the options supplied in a choice, noul (yes/no), or ordered score question. It accepts text or JSON state and optional images. This is a decision checkpoint with a 255-option readout, not a text-generation checkpoint. This configuration edition uses MatildaJevModel, MatildaJevConfig, and MATILDA tokenizer/processor classes. Use the bundled runtime below, or load the custom AutoClasses with trustremotecode=True. The package includes the backbone weights, decision readout, tokenizer, preprocessing configuration and calibrated temperature. On AMD MI355X with Python 3.12, Transformers 5.17.0 and PyTorch 2.14.0: - 25/25 smoke-test…

Model · Zero-shot classification

matilda-jev-v1.5

Maincode

A BF16 JEV decision model with a 255-option readout. This is the highest-scoring checkpoint in the completed MATILDA JEV experiments as of 9 October 2026: Decision Index 63.47 (0.3 public suite). The model accepts a state and one or more decision questions. It supports choice, noul (probability of true), and ordinal score. Inference is one forward pass per question; the model has no text-generation head and does not generate a chain of thought. The LoRA updates are already merged into the weights. No adapter merge is required. Use Python 3.12 or newer and install PyTorch for your accelerator. The validated cluster environment uses PyTorch 2.14.0, Transformers 5.17.0, and an AMD Instinct…

Model · Zero-shot classification

d3-oQ8e

Jun Kim

This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of vllm-sr/d3, the 27B multimodal decision model of Decision 3.0 from vLLM Semantic Router, built on Qwen/Qwen3.8-27B. It reads text or JSON with optional images and videos and returns a probability for every option of choice, yes/no and score questions without generating text. It includes the language backbone, the vision encoder and the code readout. The code readout (readout.safetensors, FP32) and decisionconfig.json are copied unchanged from the source. oMLX serves this checkpoint through POST /v1/systemone; Decision 3.0 support…

d3-W4A16-G128-AWQ is an unofficial INT4 weight-only (W4A16) AWQ quantization of d3, the 27B multimodal foundation decision model of Decision 3.0. It takes the same inputs and questions as d3 and returns a probability for every answer, without generating text, with 16.5 GB of weights instead of 52 GB. - Keeps 97.76% of d3's answers on the 14,031 test requests of tasksource-jev-typed-decisions, with accuracy 83.09% against BF16's 83.25%. - 16.9 GB of GPU memory after loading instead of 52.3 GB, and 17.5 GB at the peak of the 20 longest of 1,000 text requests (up to 5,928 characters of state) on an MI300X. - Reads images and videos as d3 does: the vision tower is unchanged, and d3's example…

Bobcat Flash 1.2 · Bobcat Flash 1.2 FP8 · Try it in the Space · Code on GitHub Bobcat Flash is the fast tier of Bobcat, a typed-decision model. You send a state (text or JSON) and questions whose possible answers you define in the request; Flash returns a probability for every answer you named, and nothing else. It never generates text: it reads the logits of the offered candidates at the first answer position of one forward pass, and the host builds a closed JSON reply from your own names. An answer can be wrong, but it cannot be malformed. This repository, sanghwa-na/bobcat-flash-1.2, holds Bobcat Flash 1.2 as ready-to-serve BF16 weights on google/gemma-4-26B-A4B-it (Apache-2.0) at…

A pre-quantized FP8 checkpoint of Bobcat Flash 1.2, the fast tier of the typed-decision model Bobcat: you send a state and questions whose answers you name, and it returns a probability for every answer you named; it never generates text. Evaluation, training data, limitations and the full license notes are on the main card, This repository, sanghwa-na/bobcat-flash-1.2-fp8, is the BF16 weights of sanghwa-na/bobcat-flash-1.2 quantized to FP8 (27.2 GB, FP8DYNAMIC, every expert included): float8e4m3fn weights with one scale per output channel, activations quantized per token at run time, in the compressed-tensors format that vLLM loads as it is. It is the checkpoint the Bobcat Flash Space…