Instructions to use Vishva007/d1-3B-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vishva007/d1-3B-W4A16-AutoRound with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Vishva007/d1-3B-W4A16-AutoRound", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Vishva007/d1-3B-W4A16-AutoRound", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("Vishva007/d1-3B-W4A16-AutoRound", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vishva007/d1-3B-W4A16-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vishva007/d1-3B-W4A16-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/d1-3B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Vishva007/d1-3B-W4A16-AutoRound

SGLang

How to use Vishva007/d1-3B-W4A16-AutoRound with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vishva007/d1-3B-W4A16-AutoRound" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/d1-3B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vishva007/d1-3B-W4A16-AutoRound" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/d1-3B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Vishva007/d1-3B-W4A16-AutoRound with Docker Model Runner:
```
docker model run hf.co/Vishva007/d1-3B-W4A16-AutoRound
```

d1-3B-W4A16-AutoRound / README.md

Vishva007

Update README.md

cfc78c2 verified 5 days ago

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- ar
	- zh
	- en
	- fr
	- de
	- hi
	- id
	- it
	- ja
	- ko
	- pl
	- pt
	- ru
	- es
	- th
	- vi
	pipeline_tag: image-text-to-text
	base_model:
	- LiquidAI/d1-3B
	tags:
	- liquid
	- lfm2.5
	- edge
	- decision
	- classification
	- calibration
	- system-one
	- multimodal
	- decision-model
	---


	# d1-3B-W4A16-AutoRound

	This repository contains a 4-bit W4A16 quantized version of [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B), optimized using [AutoRound](https://github.com/intel/auto-round).

	`d1-3B` is a multimodal decision model built on `LFM2.5-VL-3B`. It evaluates states (text, images, or JSON) against typed questions in a single forward pass with zero autoregressive output tokens.

	## Quantization Details

	- Method: AutoRound (`W4A16`)
	- Group Size: 32 (`group_size=32`, `sym=True`)
	- Calibration: 512 samples, 800 tuning iterations, sequence length 2048
	- Vision Tower: Retained in unquantized precision (`quant_nontext_module=False`) to preserve full visual feature fidelity
	- Format: Compatible with `auto_round` and AutoGPTQ backends

	---

	## Quickstart

	### Installation

	```bash
	pip install --upgrade "transformers>=5.14" torch torchvision pillow auto-round
	# Optional for optimized kernels:
	pip install auto-round-lib

	```

	### Inference Example

	```python
	import torch
	from transformers import AutoModel
	from transformers.image_utils import load_image

	device = "cuda" if torch.cuda.is_available() else "cpu"
	dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32

	# Load quantized model
	model = AutoModel.from_pretrained(
	"Vishva007/d1-3B-W4A16-AutoRound",
	trust_remote_code=True,
	dtype=dtype,
	device_map="auto"
	)

	# 1. Text Classification / Decision
	questions = {
	"refund": {
	"type": "noul",
	"instructions": "Is the customer asking for a refund?",
	},
	"team": {
	"type": "choice",
	"instructions": "Which team should handle this?",
	"criteria": {
	"billing": "Charges, refunds, invoices",
	"technical": "App or site faults",
	"fraud": "Suspected unauthorised use",
	},
	},
	}

	prompt = "I was charged twice this month, please refund one of them."
	res_text = model.system_one(prompt, questions)
	print("Text Decision:", res_text)

	# 2. Multimodal / Image Evaluation
	image = load_image("[http://images.cocodataset.org/val2017/000000039769.jpg](http://images.cocodataset.org/val2017/000000039769.jpg)")
	cats_q = {
	"type": "choice",
	"instructions": "How many cats are there?",
	"criteria": {"one": "One", "two": "Two", "more": "Three or more"},
	}

	res_image = model.system_one(None, {"cats": cats_q}, images=[image])
	print("Vision Decision:", res_image)

	```

	---

	## Intended Use

	* Single-pass decision classification (Yes/No `noul`, multi-choice `choice`, and calibrated scales `score`)
	* Triage, routing, intent detection, content moderation, agent safety guards
	* Zero-token generation latency overhead at reduced VRAM footprint

	---

	### 🚀 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](https://runpod.io?ref=iabrlp7z) and get $5–$500 in free credits when you add your first $10.

	#### PyTorch 2.14

	\| Template \| CUDA Version \| Docker Image \| Template ID \| Deploy \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| PyTorch 2.14 (CUDA 12.6) \| 12.6 \| `vishva123/cuda-12.6-pytorch-2.14-runpod` \| `d7lxsa4w9m` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=d7lxsa4w9m&ref=iabrlp7z) \|
	\| PyTorch 2.14 (CUDA 13.0) \| 13.0 \| `vishva123/cuda-13.0-pytorch-2.14-runpod` \| `yk0y6j6rpg` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=yk0y6j6rpg&ref=iabrlp7z) \|
	\| PyTorch 2.14 (CUDA 13.2) \| 13.2 \| `vishva123/cuda-13.2-pytorch-2.14-runpod` \| `gsp4gwx0nw` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=gsp4gwx0nw&ref=iabrlp7z) \|

	#### PyTorch 2.13

	\| Template \| CUDA Version \| Docker Image \| Template ID \| Deploy \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| PyTorch 2.13 (CUDA 12.6) \| 12.6 \| `vishva123/cuda-12.6-pytorch-2.13-runpod` \| `gmlupxnxfk` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=gmlupxnxfk&ref=iabrlp7z) \|
	\| PyTorch 2.13 (CUDA 13.0) \| 13.0 \| `vishva123/cuda-13.0-pytorch-2.13-runpod` \| `y3j8xvk4f4` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=y3j8xvk4f4&ref=iabrlp7z) \|
	\| PyTorch 2.13 (CUDA 13.2) \| 13.2 \| `vishva123/cuda-13.2-pytorch-2.13-runpod` \| `vigpissn5w` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=vigpissn5w&ref=iabrlp7z) \|

	#### PyTorch 2.12

	\| Template \| CUDA Version \| Docker Image \| Template ID \| Deploy \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| PyTorch 2.12 (CUDA 12.6) \| 12.6 \| `vishva123/cuda-12.6-pytorch-2.12-runpod` \| `ctmz86zmf0` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=ctmz86zmf0&ref=iabrlp7z) \|
	\| PyTorch 2.12 (CUDA 13.0) \| 13.0 \| `vishva123/cuda-13.0-pytorch-2.12-runpod` \| `qjko5yiwzi` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=qjko5yiwzi&ref=iabrlp7z) \|
	\| PyTorch 2.12 (CUDA 13.2) \| 13.2 \| `vishva123/cuda-13.2-pytorch-2.12-runpod` \| `ifg6xmye0f` \| [![Deploy to RunPod](https://img.shields.io/badge/RunPod-Deploy%20Pod-purple?logo=runpod&logoColor=white)](https://runpod.io/console/deploy?template=ifg6xmye0f&ref=iabrlp7z) \|

	---

	## Acknowledgements

	* Base model by [Liquid AI](https://huggingface.co/LiquidAI).
	* Quantized using Intel's [AutoRound](https://github.com/intel/auto-round).