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

File size: 1,449 Bytes

d4a1f95

{
  "validation_kind": "offline CPU metadata, tokenizer, and optional meta-device shape checks",
  "python": "3.10.18",
  "versions": {
    "transformers": "5.8.0",
    "torch": "2.8.0",
    "datasets": "3.6.0",
    "huggingface_hub": "1.14.0",
    "sentencepiece": "0.2.1",
    "tokenizers": "0.22.1",
    "safetensors": "0.6.2",
    "regex": "2026.4.4",
    "packaging": "25.0",
    "jsonschema": "4.25.1"
  },
  "config": {
    "class": "Qwen3_5MoeConfig",
    "model_type": "qwen3_5_moe",
    "architecture": [
      "Qwen3_5MoeForConditionalGeneration"
    ],
    "loaded_without_internal_packages": true
  },
  "tokenizer": {
    "class": "InternS1Tokenizer",
    "vocab_size": 251174,
    "roundtrip": "passed",
    "chat_template": "passed",
    "chat_tokens": 20,
    "prompt_tail": "<|im_start|>user\n证明群元素与其逆元具有相同的阶。<|im_end|>\n<|im_start|>assistant\n<think>\n"
  },
  "public_architecture_shape_check": {
    "expected_core_tensors": 1026,
    "checkpoint_tensors": 1811,
    "missing_core_keys": 0,
    "shape_mismatches": 0,
    "unexpected_keys": 785,
    "all_unexpected_keys_are_mtp": true,
    "core_parameters_match": true
  },
  "full_weight_loading": "not_run",
  "generation": "not_run",
  "mtp_note": "All original MTP tensors are retained; the tested public architecture has no matching parameters for them. Shape compatibility alone does not validate from_pretrained loading or inference."
}