Model reference · open weights

AdvancedMathBench-AutoVerifier

NEW · this week LLMs internlm Vision + text 1 build Licence not stated 0 dl/mo

AdvancedMathBench-AutoVerifier is an open-weight language model from internlm. AdvancedMathBench-AutoVerifier (BF16) weighs 73.0 GB; the smallest configuration that runs it is A100 80 GB.

What it is

Released byinternlm
TypeLanguage models
TaskVision + text
Parameters (lead)36.0B
Context262,144 tokens
Runs withtransformers
Released2026-09-29
Popularity0 downloads / month
Weights73.0 GB (AdvancedMathBench-AutoVerifier (BF16), file size)
LicenceLicence not stated

What it runs on

Memory and cards for AdvancedMathBench-AutoVerifier (BF16)

Weights 73.0 GB (file size) · KV cache 20 MB per 1,000 tokens of context, at 16 bits (vLLM's default for this build; an 8-bit cache halves it) · 129 MB of fixed state per request · runtime overhead from 2.5 GB on a small card · context up to 262,144 tokens.

CardRequests at once
8K tokens each
Requests at once
32K tokens each
Longest single
request
Counted
memory
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
———
A100 80 GB93125K78.2 GB
H100 80 GB———78.1 GB
RTX PRO 6000 Blackwell 96 GB4014all 256K93.8 GB
DGX Spark (GB10) 128 GB unified8531all 256K107 GB
H200 141 GB18970all 256K138 GB
B200 180 GB311115all 256K176 GB
2× L40S 48 GB
tensor parallel
3312all 256K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel
227248K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
237252K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
8828all 256K31.0 GB a card
2× H100 80 GB
tensor parallel
22081all 256K78.1 GB a card
2× A100 80 GB
tensor parallel
26498all 256K78.2 GB a card
2× RTX PRO 6000 Blackwell 96 GB
tensor parallel
326121all 256K93.8 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
175.7 GB76.2 GB
576.9 GB79.4 GB
877.8 GB81.8 GB
1680.2 GB88.2 GB
3284.9 GB101 GB
6494.4 GB127 GB

On one card, with vLLM's small-card settings (2,048 tokens a step). Cards of 70 GB and more reserve more per request and more overhead — each row above uses its own card's settings.

Estimates, not measurements, checked against published vLLM startup logs. The weights are the build's file size; the cache is calculated from its config (hybrid: linear attention with full attention every few layers); the overhead is an estimate of vLLM's own memory with that card's default settings. "Requests at once" is how many requests of that length vLLM admits — its reservation at full length, with --max-model-len set to that length; requests that stay shorter fit more. "Longest single request" is the most one request can hold there: below the model's maximum, vLLM starts only with --max-model-len set at or under it. "Counted memory" is vLLM's default 92 % of what CUDA reports for the card (the DGX Spark: about 100 GiB of its shared 128 GB). A tensor-parallel split pools the cards' memory and speeds each token up, at the cost of the link between them; a layer split (llama.cpp) holds more but does not make one request faster. Assumes vLLM 0.10 or later.

From the model card

What internlm says about AdvancedMathBench-AutoVerifier

Paper · HF Paper · GitHub · Dataset · AutoVerifier

AutoVerifier evaluates natural-language mathematical proofs, explains errors, and identifies the earliest incorrect step. It serves as the automatic grader for AdvancedMathBench's ProverBench.

Read the full model card

Model

  • Architecture: Qwen3_5MoeForConditionalGeneration.
  • Tokenizer: bundled InternS1Tokenizer; requires sentencepiece and trust_remote_code=True after reviewing the tokenizer code.
  • Weights: 40 safetensors shards, approximately 68 GiB.

Input

Use proof_verifier.md with a problem, an optional reference solution, and a candidate proof split into zero-indexed steps. The following constructs the input without loading the model weights:

from pathlib import Path
from transformers import AutoTokenizer

model_dir = "."  # Local model repository directory
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)

steps = ["A candidate proof step.", "Another candidate proof step."]
proof = "\n\n".join(
    f"\n\n{step}\n\n" for i, step in enumerate(steps)
)
template = Path(model_dir, "prompts/proof_verifier.md").read_text(encoding="utf-8")
prompt = template.format(
    problem="The mathematical problem.", human_solution="", solution=proof,
)
text = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=False, add_generation_prompt=True, enable_thinking=True,
)

Output and scoring

The final response contains an assessment, identified errors, and the first error index. For example, a no-error judgment is:

-1 means no error was found; nonnegative indices identify the earliest error, starting from 0. Parse the final answer after `` when present.

ProverBench checks each proof 8 times and accepts it only when all eight valid judgments report -1. Missing or malformed judgments do not count as acceptance. Sampling settings are documented in evaluation_settings.json.

Notes

AutoVerifier is a learned grader, not a formal proof checker, and can make errors. Tested package versions and validation scope are recorded in compatibility.json. License notices are provided in LICENSE and NOTICE.md.

Citation

If you use AdvancedMathBench AutoVerifier in your research, please cite:

@misc{kong2026advancedmathbenchbenchmarksuiteadvanced,
  title = {AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification},
  author = {Lingkai Kong and Zijian Wu and Yuzhe Gu and Haiteng Zhao and Wenyong Huang and Shuang Sun and Zhicheng Xiong and Xiaotian Zhang and Shuya Zhao and Yan Wang and Disheng Xu and Wenwei Zhang and Kai Chen},
  year = {2026},
  eprint = {2607.11849},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  doi = {10.48550/arXiv.2607.11849},
  url = {https://arxiv.org/abs/2607.11849}
}

Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.

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