Instructions to use internlm/AdvancedMathBench-AutoVerifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/AdvancedMathBench-AutoVerifier with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/AdvancedMathBench-AutoVerifier")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("internlm/AdvancedMathBench-AutoVerifier")
model = AutoModelForMultimodalLM.from_pretrained("internlm/AdvancedMathBench-AutoVerifier", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/AdvancedMathBench-AutoVerifier with vLLM:

Install from pip and serve model

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

SGLang

How to use internlm/AdvancedMathBench-AutoVerifier 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 "internlm/AdvancedMathBench-AutoVerifier" \
    --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": "internlm/AdvancedMathBench-AutoVerifier",
		"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 "internlm/AdvancedMathBench-AutoVerifier" \
        --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": "internlm/AdvancedMathBench-AutoVerifier",
		"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 internlm/AdvancedMathBench-AutoVerifier with Docker Model Runner:
```
docker model run hf.co/internlm/AdvancedMathBench-AutoVerifier
```

AdvancedMathBench-AutoVerifier

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# Preserved notices and release provenance

The original checkpoint's `LICENSE` file is included without modification.
It contains Apache License 2.0 and an Alibaba Cloud copyright notice.
`tokenization_interns1.py` retains the Intern team and Shanghai AI Lab notice
and all copied-code attributions present in the supplied file.

The checkpoint README has been replaced with an AutoVerifier-specific model
card. Its original general-model description is retained only in the local
preparation audit directory and is not represented as this verifier's results.

All model weights, model/tokenizer configuration files, processor files, and
the chat template are unmodified. The `merge_ratio.json` preparation metadata
is retained in the local audit directory, not in this runtime package.

Exact upstream model lineage and authorization for the final model release
must be confirmed by the owners. The inherited license text is not a license
grant for the separate AdvancedMathBench dataset.