Model reference · open weights

Kimina-Autoformalizer

LLMs AI-MO Text gen 1 build Open weights 544 dl/mo

Kimina-Autoformalizer is an open-weight language model from AI-MO. Kimina-Autoformalizer-7B (BF16) weighs 15.2 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

  • Kimina-Autoformalizer-7B is a 7.6B parameter text-generation model developed by AI-MO for translating natural language descriptions of competition-style problems into Lean 4 code.
  • The model supports a context length of 32,768 tokens and operates in English.
  • It is released under the apache-2.0 license.

Summary of the AI-MO/Kimina-Autoformalizer-7B model card, 2026-10-01

What it is

Released byAI-MO
Released2025-04-13
Parameters7.6B
VRAM15.2 GB for the weights

What it runs on

Memory and cards for Kimina-Autoformalizer-7B (BF16)

15.2 GBweights, file size
57 MBcache per 1K tokens
799 MBruntime overhead, at least
32,768 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
———
RTX 3090 24 GB153all 32K23.4 GB
RTX 4090 24 GB153all 32K23.4 GB
RTX 5090 32 GB317all 32K31.0 GB
L40S 48 GB5914all 32K44.0 GB
A100 80 GB13233all 32K78.2 GB
H100 80 GB12230all 32K78.1 GB
RTX PRO 6000 Blackwell 96 GB15639all 32K93.8 GB
DGX Spark (GB10) 128 GB unified18446all 32K107 GB
H200 141 GB25062all 32K138 GB
B200 180 GB32982all 32K176 GB
2× RTX 3060 12 GB
tensor parallel
133all 32K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
297all 32K15.4 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
116.5 GB17.9 GB
518.4 GB25.4 GB
819.8 GB31.1 GB
1623.5 GB46.1 GB
3231.1 GB76.2 GB
6446.1 GB136 GB

One card, with vLLM's small-card settings.

From the model card

What AI-MO says about Kimina-Autoformalizer

Read the model card

Kimina-Autoformalizer-7B is a autoformalizer model developed by Project Numina, focusing on translating natural language descriptions of competition style problems to Lean 4 code ending with by sorry.

Quick Start with vLLM

You can easily do inference using vLLM:

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_name = "AI-MO/Kimina-Autoformalizer-7B"
model = LLM(model_name)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

problem = "The volume of a cone is given by the formula $V = \frac{1}{3}Bh$, where $B$ is the area of the base and $h$ is the height. The area of the base of a cone is 30 square units, and its height is 6.5 units. What is the number of cubic units in its volume? The answer is 65."

prompt = "Please autoformalize the following problem in Lean 4 with a header. Use the following theorem names: my_favorite_theorem.\n\n"
prompt += problem

messages = [
    {"role": "system", "content": "You are an expert in mathematics and Lean 4."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=2048)
output = model.generate(text, sampling_params=sampling_params)
output_text = output[0].outputs[0].text
print(output_text)

Citation

If you find our work helpful, you can cite our paper: https://github.com/MoonshotAI/Kimina-Prover-Preview

@article{kimina_prover_2025,
	title = {Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning},
	author = {Wang, Haiming and Unsal, Mert and Lin, Xiaohan and Baksys, Mantas and Liu, Junqi and Santos, Marco Dos and Sung, Flood and Vinyes, Marina and Ying, Zhenzhe and Zhu, Zekai and Lu, Jianqiao and Saxcé, Hugues de and Bailey, Bolton and Song, Chendong and Xiao, Chenjun and Zhang, Dehao and Zhang, Ebony and Pu, Frederick and Zhu, Han and Liu, Jiawei and Bayer, Jonas and Michel, Julien and Yu, Longhui and Dreyfus-Schmidt, Léo and Tunstall, Lewis and Pagani, Luigi and Machado, Moreira and Bourigault, Pauline and Wang, Ran and Polu, Stanislas and Barroyer, Thibaut and Li, Wen-Ding and Niu, Yazhe and Fleureau, Yann and Hu, Yangyang and Yu, Zhouliang and Wang, Zihan and Yang, Zhilin and Liu, Zhengying and Li, Jia},
	year = {2025},
	url = {http://arxiv.org/abs/2504.11354},
}

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

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