Vishva007/d3-edge-W4A16-AutoRound

This repository hosts a high-precision W4A16 quantized version of vllm-sr/d3-edge using Intel AutoRound.

d3-edge is the 0.59B multimodal foundation decision model of Decision 3.0 (vLLM Semantic Router). It evaluates inputs (text, JSON, images, and video clips) against decision questions (Choice, Yes/No, Score) and outputs direct probabilities in a single forward pass without generating tokens.

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Quantization Details

The quantization was performed using AutoRound with a high-accuracy, production-grade configuration. To preserve zero-shot vision and video grounding fidelity, the vision encoder was explicitly kept unquantized.

Tuning Configuration

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Installation

Ensure you have the required dependencies and compatible transformers installed:

pip install "transformers==5.17.0" torch torchvision pillow opencv-python-headless safetensors accelerate
pip install auto-round
# Optional for linear-attention speedups:
pip install flash-linear-attention

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Quickstart & Usage

You can load and query the model directly via the standard system_one decision API.

1. Pure Text Routing

import json
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "Vishva007/d3-edge-W4A16-AutoRound", 
    trust_remote_code=True,
    device_map="auto"
)

result = model.system_one(
    state="The order arrived damaged yesterday. The customer has a receipt and asks for a replacement today.",
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this request?",
            "criteria": {
                "returns": "Refunds, replacements and damaged deliveries",
                "billing": "Payments, invoices and charges",
                "technical": "Product setup and faults"
            }
        },
        "receipt": {
            "type": "noul",
            "instructions": "Does the customer have a receipt?"
        },
        "urgency": {
            "type": "score",
            "instructions": "How urgent is this request?",
            "criteria": ["Routine", "Soon", "Today"]
        }
    },
)

print(json.dumps(result["answers"], indent=2))

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2. Multimodal Routing (Images & Video)

Because the vision tower is preserved in unquantized precision (quant_nontext_module: False), full visual resolution and feature fidelity are retained:

from huggingface_hub import hf_hub_download

# Download sample asset from original repo
receipt_img = hf_hub_download("vllm-sr/d3-edge", "assets/example-receipt.png")

result = model.system_one(
    state="The customer says the blender arrived cracked and attached the receipt.",
    images=[receipt_img],  # Accepts paths, URLs, PIL images, or base64 strings
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this request?",
            "criteria": {
                "returns": "Refunds, replacements and damaged deliveries",
                "billing": "Payments, invoices and charges"
            }
        },
        "on_receipt": {
            "type": "noul",
            "instructions": "Does the receipt list the blender?"
        }
    },
)

print(result["answers"])

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Memory & Performance Advantages

• VRAM Footprint: Reduces base language backbone weight footprint by roughly 3–3.5× compared to standard BF16.
• Edge Deployment: Designed to run efficiently on low-memory edge accelerators, discrete workstation GPUs, or embedded hardware.
• Visual Accuracy: Zero degradation in visual QA benchmarks (CV-Bench, Perception Test) due to unquantized vision tower caching.

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🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

PyTorch 2.15

PyTorch 2.14

PyTorch 2.13

PyTorch 2.12

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License & Attribution

• Base Model: vllm-sr/d3-edge (Apache-2.0) by the vLLM Semantic Router Team.
• License: Apache-2.0.