Instructions to use autotrust/AutoTrust-Decider-Pro-Vision-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autotrust/AutoTrust-Decider-Pro-Vision-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="autotrust/AutoTrust-Decider-Pro-Vision-27B")
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/AutoTrust-Decider-Pro-Vision-27B")
model = AutoModelForMultimodalLM.from_pretrained("autotrust/AutoTrust-Decider-Pro-Vision-27B", 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/AutoTrust-Decider-Pro-Vision-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "autotrust/AutoTrust-Decider-Pro-Vision-27B"
# 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/AutoTrust-Decider-Pro-Vision-27B",
		"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/AutoTrust-Decider-Pro-Vision-27B

SGLang

How to use autotrust/AutoTrust-Decider-Pro-Vision-27B 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/AutoTrust-Decider-Pro-Vision-27B" \
    --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/AutoTrust-Decider-Pro-Vision-27B",
		"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/AutoTrust-Decider-Pro-Vision-27B" \
        --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/AutoTrust-Decider-Pro-Vision-27B",
		"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/AutoTrust-Decider-Pro-Vision-27B with Docker Model Runner:
```
docker model run hf.co/autotrust/AutoTrust-Decider-Pro-Vision-27B
```

cloudyu commited on Oct 2

Commit

d835ee0

verified ·

1 Parent(s): 8928e07

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

Browse files

Files changed (1) hide show

serve_decide.py +3 -3

serve_decide.py CHANGED Viewed

@@ -12,8 +12,8 @@ POST /v1/decide
   options    list of strings (choice only)
   strategy   choices with more than 16 options: "single" (one pass, labels A-P then Q-Z, AA, ...), "tournament"
              (groups of <=16 + a final of 16), "permute" (single pass over 4 option orders, averaged); default per model
-  thinking   "off", "auto" (think only when the leading option is below `threshold`), "on" (always think);
-             default per model (GEV: "auto"; JEV-27B: "off")
   threshold  System 1 confidence below which "auto" switches thinking on (default per model)
   reasoning controls, as for the base model's own chat API:
     chat_template_kwargs  passed to the base model's chat template (e.g. Qwen3.8: {"reasoning_effort": "low"})
@@ -65,7 +65,7 @@ PROFILES = {
     "qwen": {"prefix": "", "image": "<|vision_start|><|image_pad|><|vision_end|>", "end_think": "</think>",
              "after_think": "\n\n", "strategy": "single", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
     "gemma": {"prefix": "<bos>", "image": "<|image|>", "end_think": "<channel|>", "after_think": "",
-              "strategy": "tournament", "threshold": 0.8, "mix": 0.5, "thinking": "auto"},
 }
 # Read the full distribution: override generation_config defaults (top_k/top_p) that would truncate processed logprobs.
 READ = dict(max_tokens=1, temperature=1.0, top_p=1.0, top_k=0, min_p=0.0, repetition_penalty=1.0,

   options    list of strings (choice only)
   strategy   choices with more than 16 options: "single" (one pass, labels A-P then Q-Z, AA, ...), "tournament"
              (groups of <=16 + a final of 16), "permute" (single pass over 4 option orders, averaged); default per model
+  thinking   "off" (default: System 1 only), "auto" (think only when the leading option is below `threshold`),
+             "on" (always think)
   threshold  System 1 confidence below which "auto" switches thinking on (default per model)
   reasoning controls, as for the base model's own chat API:
     chat_template_kwargs  passed to the base model's chat template (e.g. Qwen3.8: {"reasoning_effort": "low"})
     "qwen": {"prefix": "", "image": "<|vision_start|><|image_pad|><|vision_end|>", "end_think": "</think>",
              "after_think": "\n\n", "strategy": "single", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
     "gemma": {"prefix": "<bos>", "image": "<|image|>", "end_think": "<channel|>", "after_think": "",
+              "strategy": "tournament", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
 }
 # Read the full distribution: override generation_config defaults (top_k/top_p) that would truncate processed logprobs.
 READ = dict(max_tokens=1, temperature=1.0, top_p=1.0, top_k=0, min_p=0.0, repetition_penalty=1.0,