Webs-Sejong-31B
🏆
Ranked #1 on the K-AI Leaderboard
(
leaderboard.aihub.or.kr)
Leaderboard entry:
Webs-Sejong-31B-R1
Public release:
websfactory/Webs-Sejong-31B-v7
Overall average
0.624, ranked
#1 as of 2026-07-05.
Evaluated on the K-AI Leaderboard, a public Korean LLM evaluation platform
operated through AI Hub / NIA, using non-public benchmark data that is not
disclosed to participants.
Webs-Sejong-31B is a 31B-parameter Korean-centric language model based on
google/gemma-4-31B-it. It is strong at Korean-language knowledge, Korean
cultural context, professional and academic reasoning, and commonsense QA, while
retaining English capability. This repository provides the same checkpoint that
was submitted as Webs-Sejong-31B-R1 on the K-AI Leaderboard.
Highlights
- #1 on the K-AI Leaderboard. Overall average 0.624, the top score on the
public board as of 2026-07-05 (leaderboard entry:
Webs-Sejong-31B-R1).
- Korean-first. Strong on Korean cultural and academic tasks, with English
ability retained.
- Drop-in Gemma-4. Standard Gemma-4 architecture and tokenizer: compatible
with the Hugging Face
transformers Gemma-4 implementation and expected to
work with Gemma-4-compatible serving stacks.
Evaluation: K-AI Leaderboard
Evaluated on the K-AI Leaderboard, a public Korean LLM evaluation platform
operated through AI Hub / NIA. Scores are produced on non-public benchmark data
that is not disclosed to participants.
| |
|---|
| Leaderboard entry | Webs-Sejong-31B-R1 |
| Overall average | 0.624 |
| Rank | #1 (as of 2026-07-05) |
Because the benchmark data is not disclosed to participants, this reduces the
likelihood of direct benchmark overfitting. Users should still evaluate the model
on their own target tasks.
Model
| |
|---|
| Architecture | Gemma-4-31B (dense) |
| Parameters | ~31B |
| Precision | bfloat16 |
| Languages | Korean (primary), English |
| Base model | google/gemma-4-31B-it |
Hardware
At bf16 the weights are roughly 62 GB. Practical setups:
- Full precision: one 80 GB GPU (A100 / H100), or two 40–48 GB GPUs.
- 4-bit quantized: roughly 20–24 GB for the quantized weights; allow extra
memory for KV cache, context length, batch size, and image inputs.
Usage
1from transformers import AutoModelForImageTextToText, AutoProcessor
2
3model_id = "websfactory/Webs-Sejong-31B-v7"
4processor = AutoProcessor.from_pretrained(model_id)
5model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")
This model follows the standard Gemma-4 interface. For image-and-text input
formatting, refer to the base model documentation at
google/gemma-4-31B-it.
Training Details
Training and adaptation details are proprietary and are not disclosed in this
release.
Intended Use & Limitations
Intended for Korean-language assistance, knowledge QA, and reasoning. Like any
language model it can produce incorrect or outdated information, so do not rely on
it for medical, legal, financial, or public-policy decisions without human review.
Evaluate it on your own target tasks before production deployment.
License
This model is a derivative of Gemma-4 and is distributed under the
Gemma Terms of Use. By using this model
you agree to those terms and Google's Prohibited Use Policy.
