Instructions to use autotrust/JEV-27B-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autotrust/JEV-27B-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="autotrust/JEV-27B-VL")
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("autotrust/JEV-27B-VL")
model = AutoModelForMultimodalLM.from_pretrained("autotrust/JEV-27B-VL", 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 autotrust/JEV-27B-VL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "autotrust/JEV-27B-VL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "autotrust/JEV-27B-VL",
		"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/autotrust/JEV-27B-VL

SGLang

How to use autotrust/JEV-27B-VL 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 "autotrust/JEV-27B-VL" \
    --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": "autotrust/JEV-27B-VL",
		"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 "autotrust/JEV-27B-VL" \
        --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": "autotrust/JEV-27B-VL",
		"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 autotrust/JEV-27B-VL with Docker Model Runner:
```
docker model run hf.co/autotrust/JEV-27B-VL
```

JEV-27B-VL

Commit History

Model card: fix the ModelScope homepage sentence (real-time sync)

3514d1d
verified

cloudyu commited on about 6 hours ago

Model card: ModelScope section — links only (no recommendation or install lines)

f01c644
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cloudyu commited on about 6 hours ago

Model card: ModelScope section heading without '(China)'

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cloudyu commited on about 6 hours ago

Model card: ModelScope (China) download links — official AutoTrust-Labs homepage, real-time sync with this repository

e42e085
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cloudyu commited on about 7 hours ago

Update README.md

ba3f0d5
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cloudyu commited on 3 days ago

Add decision-model tag (#5)

4000d23

cloudyu

victor HF Staff commited on 3 days ago

Model card: Decision Index #1 first, then the model introduction

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cloudyu commited on 3 days ago

Model card: what the Decision Index Vision board shows (strengths)

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cloudyu commited on 3 days ago

Model card: #1 on the Jev Decision Index 0.3 Vision board (top of the card)

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cloudyu commited on 3 days ago

Add robot-arm and computer-use demos (videos, results) to the model card

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cloudyu commited on 7 days ago

serve_decide.py: System 1 only by default for every model (thinking is opt-in)

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cloudyu commited on 8 days ago

serve_decide.py: one file for JEV-27B and GEV; optional adaptive thinking (thinking=auto|on, reasoning_effort, think_budget; off by default for JEV-27B), answer read-out that also works with speculative decoding

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cloudyu commited on 8 days ago

Update README.md

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cloudyu commited on 9 days ago

Model card: "New (1 October 2026)" summary at the top

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cloudyu commited on 9 days ago