Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
quantized
compressed-tensors
nvfp4
custom_code
8-bit precision
Instructions to use vllm-sr/d3-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-NVFP4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-NVFP4", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-NVFP4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Card: tasksource test split through the model runtime (82.71%, 96.64% agreement)
Browse files
README.md
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- **Scores 64.83 on the Jev Decision Index 0.3 public suite** against d3's 65.45, giving d3's top answer on 96.95% of its 312,320 questions; every area within 1.17 points of d3.
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- **Half of d3's latency on a Blackwell GPU, a third of its memory:** 28.5 ms against 62.6 ms per request (median) on one RTX PRO 6000 in the vLLM Semantic Router model runtime with NVFP4 support and graphs replaying, in 17.2 GB of GPU memory instead of 52.2 GB.
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- **Reads images and videos** as d3 does: the vision tower is unchanged. On the vision board's public suite it scores 73.60 against d3's 74.15 on the same machine (93.88% of d3's answers), and on Perception Test 76.57% against 77.73% (93.43%).
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- **tasksource-jev-typed-decisions
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## Quickstart
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| | d3 (BF16) | d3-NVFP4 | Top-1 agreement with d3 |
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| Accuracy | 83.
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Every usable row of the split at revision `28c39125` (a state of at most 6,000 characters, no repeated options): 14,031 requests of one question each
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### Images: Jev Decision Index vision board 0.3.2
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- **Scores 64.83 on the Jev Decision Index 0.3 public suite** against d3's 65.45, giving d3's top answer on 96.95% of its 312,320 questions; every area within 1.17 points of d3.
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- **Half of d3's latency on a Blackwell GPU, a third of its memory:** 28.5 ms against 62.6 ms per request (median) on one RTX PRO 6000 in the vLLM Semantic Router model runtime with NVFP4 support and graphs replaying, in 17.2 GB of GPU memory instead of 52.2 GB.
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- **Reads images and videos** as d3 does: the vision tower is unchanged. On the vision board's public suite it scores 73.60 against d3's 74.15 on the same machine (93.88% of d3's answers), and on Perception Test 76.57% against 77.73% (93.43%).
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- **Keeps 96.64% of d3's answers** on the 14,031 test requests of tasksource-jev-typed-decisions, with accuracy 82.71% against d3's 83.23%.
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## Quickstart
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| | d3 (BF16) | d3-NVFP4 | Top-1 agreement with d3 |
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| Accuracy | 83.23% | **82.71 (−0.52)%** | 96.64% (472 of 14,031 answers changed) |
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Every usable row of the split at revision `28c39125` (a state of at most 6,000 characters, no repeated options): 14,031 requests of one question each, answered by the vLLM Semantic Router model runtime with NVFP4 support on one RTX PRO 6000, d3 (BF16) by the same runtime on the same machine (83.25% on one AMD Instinct MI300X in the #5066 record). Agreement compares the top answer of every question with that BF16 run (choice and score: the highest-probability option; yes / no: p ≥ 0.5). Two calibration states, an LSAT logic-game passage and a news article, are also the states of 2 of these 14,031 requests.
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### Images: Jev Decision Index vision board 0.3.2
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