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: 1,109 Bytes

d4a1f95

{
  "purpose": "Describe settings separately; not an executable evaluation config",
  "explicit_historical_evaluation_settings": {
    "verifier_repeats_per_proof": 8,
    "max_new_tokens": 65536,
    "temperature": 1.0,
    "top_p": null,
    "top_k": null
  },
  "unchanged_checkpoint_generation_defaults": {
    "do_sample": true,
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 20,
    "eos_token_id": [248046, 248044],
    "pad_token_id": 248044
  },
  "step_index_base": 0,
  "no_error_sentinel": -1,
  "verifier_prompt": "prompts/proof_verifier.md",
  "notes": [
    "Historical API configuration did not explicitly fix top_p/top_k; runtime defaults require confirmation for exact reproduction.",
    "Checkpoint default sampling values are not asserted to be the historical serving parameters.",
    "The bundled full model includes MTP and vision tensors; this staging step does not convert weights or strip model components.",
    "The intended pessimistic decision requires eight valid no-error judgments; legacy parse-failure handling belongs to the separately versioned evaluator."
  ]
}