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category_classifier_modernbert-base_model

vllm-sr

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

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d3

vllm-sr

**d3** is the 27B 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 videos) 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. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM serves d3 natively: `vllm serve` loads this repository as it is, with no conversion, and answers on vLLM's System One endpoint, `/v1/systemone`. The support is on vLLM's main branch since 2026-10-14 (vllm-project/vllm#61565, commit `89cab55`), and no vLLM release includes it yet (the latest is v0.31.0). Until one does, install a current nightly build with uv; vLLM's nightly indexes do not work with plain `pip`:

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d3-edge

vllm-sr

**d3-edge** is the 0.6B 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 videos) 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. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM Semantic Router serves Decision 3.0 natively in Engine mode, which runs one model behind the System One API on your own machine. Its answers matched this package's own `d3_server.py` exactly, for text, images and videos, with one request per forward, the former default (measured on AMD Instinct MI325X); with batching, now the default, a few near-tie decisions change (Serving performance).

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d3-flash

vllm-sr

**d3-flash** is the 9B 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 videos) 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. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM Semantic Router serves Decision 3.0 natively in Engine mode, which runs one model behind the System One API on your own machine. Its answers matched this package's own `d3_server.py` exactly, for text, images and videos, with one request per forward, the former default (measured on AMD Instinct MI325X); with batching, now the default, a few near-tie decisions change (Serving performance).

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d3-lite

vllm-sr

**d3-lite** is the 0.8B 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 videos) 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. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM Semantic Router serves Decision 3.0 natively in Engine mode, which runs one model behind the System One API on your own machine. Its answers matched this package's own `d3_server.py` exactly, for text, images and videos, with one request per forward, the former default (measured on AMD Instinct MI325X); with batching, now the default, a few near-tie decisions change (Serving performance).

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分类待补充

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价格待核验多模态与渠道费用须分别核对

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d3-mini

vllm-sr

**d3-mini** is the 4B 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 videos) 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. **Try it free** at decision.vllm-sr.ai · Official Space: vllm-sr/Decision-3.0 - **Jev Decision Index 0.3.2: 52.6 on the official text board**, first among the entries of similar size and on the board's Pareto frontier; public suite 54.90, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - **+7.7 over Decision 2.0** (its 4B model: 45.0 on the official board): +10.7 on the public suite, ahead in all five of its areas, and +8.2 and +3.4 on the private same-skill tests and new-domain tasks. - **Reads images:** multiple images per request (PNG, JPEG or WebP), given as paths, URLs, PIL images or base64 data URLs; every question of the request sees all of them. - **Reads videos:** multiple videos per request (MP4, WebM, MOV or MKV), given as paths, URLs, base64 data URLs or frame arrays, read at 2 frames per second; **Perception Test (multiple-choice video QA): 70.3** (internal evaluation). - **Speed:** a median of 18.1 ms for a text request, 112 ms for a request with an image and 336 ms for a request with a 10-second video, on one AMD Instinct MI325X GPU, one request at a time with AutoModel. - **Serving:** 151 requests/s with p99 under 1 s on one AMD Instinct MI325X with vLLM Semantic Router, 2.7× AutoModel (one-question text, with batching, now the default); with one request per forward, the former default, it gave answers identical to AutoModel's (Serving performance). - **Many questions, one call:** Choice, Yes / No and Score questions about the same input are answered together, each from its own forward pass over the input, with a probability for every option.

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d3-nano

vllm-sr

**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 videos) 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. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM Semantic Router serves Decision 3.0 natively in Engine mode, which runs one model behind the System One API on your own machine. Its answers matched this package's own `d3_server.py` exactly, for text, images and videos, with one request per forward, the former default (measured on AMD Instinct MI325X); with batching, now the default, a few near-tie decisions change (Serving performance).

译文排队中 · 当前显示来源原文

分类待补充

输入待核验→ 输出待核验

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗