Model reference · open weights
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.
Summary of the AI-MO/Kimina-Autoformalizer-7B model card, 2026-10-01
What it is
| Released by | AI-MO |
|---|---|
| Released | 2025-04-13 |
| Parameters | 7.6B |
| VRAM | 15.2 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | — | — | — | |
| RTX 3090 24 GB | 15 | 3 | all 32K | 23.4 GB |
| RTX 4090 24 GB | 15 | 3 | all 32K | 23.4 GB |
| RTX 5090 32 GB | 31 | 7 | all 32K | 31.0 GB |
| L40S 48 GB | 59 | 14 | all 32K | 44.0 GB |
| A100 80 GB | 132 | 33 | all 32K | 78.2 GB |
| H100 80 GB | 122 | 30 | all 32K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 156 | 39 | all 32K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 184 | 46 | all 32K | 107 GB |
| H200 141 GB | 250 | 62 | all 32K | 138 GB |
| B200 180 GB | 329 | 82 | all 32K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 13 | 3 | all 32K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 29 | 7 | all 32K | 15.4 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 16.5 GB | 17.9 GB |
| 5 | 18.4 GB | 25.4 GB |
| 8 | 19.8 GB | 31.1 GB |
| 16 | 23.5 GB | 46.1 GB |
| 32 | 31.1 GB | 76.2 GB |
| 64 | 46.1 GB | 136 GB |
One card, with vLLM's small-card settings.
From 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.
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)
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.