MedJev

Model · Zero-shot classification

Vela-2.0-9B

vLLM Semantic Router

Open Foundation Routing Models Routing decisions. Safety checks. Precise text spans. The largest hybrid member of Vela 2.0: high-accuracy multilingual routing, long-document PII and hallucination detection, with open-label spans through one interface. Define options, labels and rubrics at request time. Ask multiple named questions about a request, context and answer, and receive structured decisions with the text spans that support your workflow. 1. High-accuracy decisions. A 41.63 Jev Decision Index with optional Noul calibration, plus 0.989 AUC on unseen prompt-attack families. 2. Routing and safety together. Use one request for routing, prompt-attack checks, PII and unsupported-claim…

Model · Zero-shot classification

LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned

Microsoft

Weiquan Huang 1, Aoqi Wu 1, Yifan Yang 2†, Xufang Luo 2, Yuqing Yang 2, Liang Hu 1, Qi Dai 2, Xiyang Dai 2, Dongdong Chen 2, Chong Luo 2, Lili Qiu 2 In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being…

Model · Zero-shot classification

aplomb-1

EmpirioLabs AI

Aplomb 1 is our first model, a decision model. It reads text, JSON, images, video and audio, and answers typed questions about them with calibrated probabilities instead of generated text. Send a state of up to 1M tokens with up to 128 questions, and every answer comes back as a probability distribution with a confidence value, in one request. Aplomb 1 has 5.3 billion parameters. - Every input type in one model: text, JSON objects and arrays, images, video and audio. selection (which function to call, with distributions over its enum and boolean arguments). contain what the question needs. - Zero data retention by default on the hosted API: EmpirioLabs does not retain the content of…

Model · Zero-shot classification

Vela-2.0-4B

vLLM Semantic Router

Open Foundation Routing Models Routing decisions. Safety checks. Precise text spans. The balanced hybrid member of Vela 2.0: strong multilingual safety and general decisions, with router and open-label spans through one interface. Define options, labels and rubrics at request time. Ask multiple named questions about a request, context and answer, and receive structured decisions with the text spans that support your workflow. 1. Balanced routing capability. Safety macro AUC of 0.921 across 14 public sets, alongside open-label extraction and general decisions. 2. Routing and safety together. Use one request for routing, prompt-attack checks, PII and unsupported-claim detection. 3. Decisions…

Model · Zero-shot classification

d3-nano

vLLM Semantic Router

d3-nano is the 2B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 42.14, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +12.1 on the public suite over Decision 2.0 (its 2B model: 30.05 on the board), ahead in all five areas. Images go before the text of the request, each read at…

Model · Zero-shot classification

chinese-decision-2b

Quincyqiang

Chinese Decision 是一个面向中文场景的轻量级决策模型。它不聊天、不生成文本：给定上下文和一组固定候选项，一次前向直接返回每个候选项的概率，适合意图识别、工单分流、内容审核、数据打标、给模型输出打分、智能体动作选择等"有限选项判断"任务。 - 一次前向，零输出 token：读取最后位置上候选字母 A–Z 的 logits，softmax 后即为概率。 - 三种题型：noul（是/否）、choice（单选）、score（有序等级，返回期望分值）。 - 系列中准确率最高：CJ-Bench 全量整体 ACC 84.11%，医疗 / 法律 / 金融平均 78.25%，比 chinese-decision-0.8b 分别高 1.01 / 1.35 个百分点。 - 概率经过校准：附带在独立校准划分上拟合的温度，整体 ECE 0.64%。 更新于 2026-10-17。BF16 合并模型已完成全量 CJ-Bench、保存/重载和实际 HTTP 三题型/双路由验证，并已在 Hugging Face 与 ModelScope 公开发布。由本模型直接转换的 Q80 GGUF 也已完成全量 CJ-Bench 对照，见 2B Q80 模型卡。 训练数据版本 a3963a61f71e00051a80721e587d720d8926e8e7。最终 adapter SHA256 为 9b55e8eda00997544b76fa945d890605c3acae4199041c197ef5a09275142a7c；合并权重 SHA256 为…