d3:2b

160 6 hours ago

Turn text, structured data, and images into scored choices, yes/no answers, and ratings.

vision decision 2b 4b 9b 27b 
curl http://localhost:11434/v1/systemone \
  -H "Content-Type: application/json" \
  -d '{
    "model": "d3:2b",
    "state": "Hello World",
    "questions": {
      "says_hello": {
        "type": "noul",
        "instructions": "Does the state text contain a greeting?",
        "criteria": {
          "true": "The state text contains a greeting.",
          "false": "The state text does not contain a greeting."
        }
      }
    }
  }'

Details

6 hours ago

5ae05af4804e · 3.1GB

json
{ "format_version": 1, "format_id": "d3-code-readout-v1", "prompt": "d3", "base_model": "Qwen/Qwen3.
json
{ "size": { "longest_edge": 16777216, "shortest_edge": 65536 }, "patch_size": 16, "temporal_patch_si
json
{ "version": "1.0", "truncation": null, "padding": null, "added_tokens": [ { "id": 248044, "content"
json
{ "add_prefix_space": false, "added_tokens_decoder": { "248044": { "content": "<|endoftext|>", "lstr
json
{ "size": { "longest_edge": 25165824, "shortest_edge": 4096 }, "patch_size": 16, "temporal_patch_siz
json
{ "!": 0, "\"": 1, "#": 2, "$": 3, "%": 4, "&": 5, "'": 6, "(": 7, ")": 8, "*": 9, "+": 10, ",": 11,
license
Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR US
params
{ "num_batch": 4096, "num_ctx": 4096 }
json
{ "architectures": [ "Qwen3_5Model" ], "dtype": "bfloat16", "image_token_id": 248056, "model_type":
618 tensors

Readme

d3 is a family of decision models from vLLM Semantic Router. Give it text, structured state, or a still image, then ask it to choose a category, answer yes or no, or rate something on a scale. It returns scores and probabilities for the options you provide. It does not write a conversational answer.

Use d3 to route support requests, triage documents, prioritize cases, or check whether an image meets a criterion. You can ask several questions about the same input in one request.

Get started

d3 runs the 4B model, a good starting point on a 16 GB machine. This example routes a support request:

ollama run d3 "A customer says their card was charged twice." \
  --ask "Which team should handle this?" \
  --choice 'billing=Invoices and charges' \
  --choice 'returns=Refunds and replacements' \
  --choice 'technical=App errors'

d3 requires Ollama 0.40.9 or later.

Choose a size

Model Best fit Tested Apple Silicon system Loaded model memory
d3:2b Fast, local routing and classification 8 GB 3.62 GB
d3 (4B) General use 16 GB 5.63 GB
d3:9b Harder decisions that benefit from more capacity 32 GB 10.09 GB
d3:27b Highest accuracy in the publisher’s family evaluation 64 GB 28.61 GB

Memory figures are the peak loaded model allocations measured in Ollama for the default MXFP8 packages on Apple Silicon with a 4,096-token context. The system recommendations leave room for macOS and the full test workload. On other supported systems, these names select Q8_0 packages and memory use will differ. Format-specific tags are available when you need a particular quantization.

Quality

The publisher reports these Jev Decision Index 0.3.2 scores for the original checkpoints. They are not scores for the Ollama quantized packages.

Model 2B 4B 9B 27B
Text index 38.9 52.6 55.8 66.3
Vision index 54.7 63.2 66.9 71.3

Publisher chart comparing Decision 3.0 quality and model size

Limits

  • Ollama supports text, structured state, and still images for d3. Video and Box/Track decisions are unavailable.
  • d3 scores the options and levels you supply. Clear, distinct criteria matter, especially when an answer triggers an action.
  • Scores and probabilities are model outputs, not guaranteed real-world frequencies. Check performance on examples from your own workflow before setting thresholds.
  • These packages use a 4,096-token context.

The models are released under Apache 2.0. Publisher checkpoints: 2B, 4B, 9B, 27B.