Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Eval Results
Instructions to use vllm-sr/d3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
d3 v3.0.5
Browse files- MODEL_MANIFEST.json +31 -31
- README.md +12 -12
- assets/index-areas.png +2 -2
- assets/index-pareto.png +2 -2
- model-00001-of-00011.safetensors +1 -1
- model-00002-of-00011.safetensors +1 -1
- model-00003-of-00011.safetensors +1 -1
- model-00004-of-00011.safetensors +1 -1
- model-00005-of-00011.safetensors +1 -1
- model-00006-of-00011.safetensors +1 -1
- model-00007-of-00011.safetensors +1 -1
- model-00008-of-00011.safetensors +1 -1
- model-00009-of-00011.safetensors +1 -1
- model-00010-of-00011.safetensors +1 -1
- model-00011-of-00011.safetensors +1 -1
- readout.safetensors +1 -1
MODEL_MANIFEST.json
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README.md
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## Highlights
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- **Jev Decision Index 0.3, public suite: 65.
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-
- **+8.
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- **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.
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- **Speed:** text requests take a median of 54 ms, and requests with an image a median of 277 ms, on one AMD Instinct MI325X, one request at a time.
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- **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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| Model | Jev Decision Index ↑ | Public ↑ | Same-skill tests ↑ | New-domain tasks ↑ |
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| --- | ---: | ---: | ---: | ---: |
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-
| **d3** | **64.
|
| 127 |
| Perplexity Decider v1.1 (27B) | 62.8 | 62.3 | 61.1 | 55.6 |
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| Fastino GLiDE no-thinking (28B) | 60.2 | 59.1 | 59.5 | 52.9 |
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| Jev | 60.1 | 58.0 | 58.0 | 55.0 |
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| Model | Vision Index ↑ | Public ↑ | Private ↑ |
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| --- | ---: | ---: | ---: |
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-
| **d3** | **71.6** | **74.2** | **
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| Perplexity Decider v1.1 (27B) | 70.6 | 73.2 | 67.9 |
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| 143 |
| JEV-27B-VL | 69.6 | 72.8 | 66.4 |
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| 144 |
| Solomon v1.1 (27B) | 66.8 | 69.9 | 63.8 |
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@@ -147,17 +147,17 @@ d3 on the public vision benchmarks († approximate rebuild):
|
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| Benchmark | d3 |
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| --- | ---: |
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| CV-Bench | 76.
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| BLINK | 59.
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| RealWorldQA | 73.
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| 153 |
| CharXiv † | 91.7 |
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| InfographicVQA † | 96.7 |
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| Mind2Web † | 90.0 |
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| 156 |
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| Winoground | 85.
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| 157 |
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| KIE (CORD+FUNSD) † | 97.
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| 158 |
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| Moderation (Hateful Memes) |
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| R-Bench-M |
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| MMMU-Pro vision |
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<sub>d3: internal evaluation. Others: live board data, text 2026-10-10, vision 2026-10-09.</sub>
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## Highlights
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| 30 |
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| 31 |
+
- **Jev Decision Index 0.3, public suite: 65.45**, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported.
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+
- **+8.5 on the public suite over Decision 2.0** (its 27B model: 56.97 on the board), ahead in all five areas.
|
| 33 |
- **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.
|
| 34 |
- **Speed:** text requests take a median of 54 ms, and requests with an image a median of 277 ms, on one AMD Instinct MI325X, one request at a time.
|
| 35 |
- **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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| 123 |
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| Model | Jev Decision Index ↑ | Public ↑ | Same-skill tests ↑ | New-domain tasks ↑ |
|
| 125 |
| --- | ---: | ---: | ---: | ---: |
|
| 126 |
+
| **d3** | **64.7** | **65.5** | **62.8** | **56.6** |
|
| 127 |
| Perplexity Decider v1.1 (27B) | 62.8 | 62.3 | 61.1 | 55.6 |
|
| 128 |
| Fastino GLiDE no-thinking (28B) | 60.2 | 59.1 | 59.5 | 52.9 |
|
| 129 |
| Jev | 60.1 | 58.0 | 58.0 | 55.0 |
|
|
|
|
| 138 |
|
| 139 |
| Model | Vision Index ↑ | Public ↑ | Private ↑ |
|
| 140 |
| --- | ---: | ---: | ---: |
|
| 141 |
+
| **d3** | **71.6** | **74.2** | **68.9** |
|
| 142 |
| Perplexity Decider v1.1 (27B) | 70.6 | 73.2 | 67.9 |
|
| 143 |
| JEV-27B-VL | 69.6 | 72.8 | 66.4 |
|
| 144 |
| Solomon v1.1 (27B) | 66.8 | 69.9 | 63.8 |
|
|
|
|
| 147 |
|
| 148 |
| Benchmark | d3 |
|
| 149 |
| --- | ---: |
|
| 150 |
+
| CV-Bench | 76.2 |
|
| 151 |
+
| BLINK | 59.9 |
|
| 152 |
+
| RealWorldQA | 73.4 |
|
| 153 |
| CharXiv † | 91.7 |
|
| 154 |
| InfographicVQA † | 96.7 |
|
| 155 |
| Mind2Web † | 90.0 |
|
| 156 |
+
| Winoground | 85.8 |
|
| 157 |
+
| KIE (CORD+FUNSD) † | 97.7 |
|
| 158 |
+
| Moderation (Hateful Memes) | 43.9 |
|
| 159 |
+
| R-Bench-M | 33.0 |
|
| 160 |
+
| MMMU-Pro vision | 47.0 |
|
| 161 |
|
| 162 |
<sub>d3: internal evaluation. Others: live board data, text 2026-10-10, vision 2026-10-09.</sub>
|
| 163 |
|
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