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JDONE-Research
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Model Details
Model Provider
JDONE-Research
Model Tree
google/gemma-4-31B-it
Input Modalities
Output Modalities
Supported Functionality
AIOne-Agent-52B-A36B-it is a Korean-tuned multimodal Mixture-of-Experts (MoE) model based on Gemma 4 31B IT. The model retains the full text + image + video capabilities of the base Gemma 4 family and adds a Korean-domain MoE branch that activates the right experts for the input on the fly.
top_k=2 of 8 experts) with always-on dense shared MLP. ~36 B parameters are active per token in the text backbone, while the full text backbone holds ~52 B parameters worth of capacity.The name follows the Gemma 4 convention (google/gemma-4-26B-A4B-it): the first number is the text backbone parameter count, A{X}B is the per-token active parameter count, and the vision encoder (0.57 B) is reported separately.
python
import torchfrom transformers import AutoProcessor, Gemma4ForConditionalGeneration MODEL_ID = "JDONE-Research/AIOne-Agent-52B-A36B-it" model = Gemma4ForConditionalGeneration.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(MODEL_ID) messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image.jpg"}, {"type": "text", "text": "이 사진에 무엇이 보이나요? 한국어로 답해주세요."}, ], },] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True,).to(model.device) generated = model.generate(**inputs, max_new_tokens=256, do_sample=False)print( processor.tokenizer.decode( generated[0, inputs.input_ids.shape[1]:], skip_special_tokens=True ))python
messages = [ { "role": "user", "content": [ { "type": "text", "text": "사과 3개와 배 5개의 가격이 12,000원입니다. " "사과 1개가 1,500원이라면 배 1개 가격은? 단계적으로 풀이해주세요.", }, ], },]bash
vllm serve JDONE-Research/AIOne-Agent-52B-A36B-it \ --dtype bfloat16 \ --tensor-parallel-size 4 \ --max-model-len 32768Korean math reasoning (text-only)
단계별 풀이 과정은 다음과 같습니다.
1단계: 사과 3개의 전체 가격 구하기 사과 1개의 가격이 1,500원이므로, 3개의 가격을 계산합니다.
- 1,500원 × 3개 = 4,500원
2단계: 배 5개의 전체 가격 구하기 전체 금액(12,000원)에서 사과 3개의 가격(4,500원)을 빼면 배 5개의 전체 가격이 나옵니다.
Multimodal (image + Korean caption)
다양한 색상의 점들이 섞여 무지개 빛깔의 그라데이션을 이루고 있는 이미지입니다.
| Field | Value |
|---|---|
| Architecture | Gemma4ForConditionalGeneration |
| Base model | google/gemma-4-31B-it |
| Text backbone parameters | 51.51 B → 52 B (in name) |
| Active parameters per token (text) | 35.90 B → A36B (in name) (dense MLP always on + top-2 of 8 experts + attention) |
| Vision tower | 0.57 B (SigLIP-style, 27 layers) |
| MM projector | 0.01 B |
| Total weights on disk | 52.09 B / ~104 GB (BF16) |
| MoE config | num_experts=8, top_k=2, moe_intermediate_size=2688 |
| Modality | Text + Image + Video → Text |
| Precision | bfloat16 |
| Context length | 256K |
| Languages | Korean (primary), English |
This model is released under the Apache License 2.0 license.
bibtex
@misc{aione_agent_52b_a36b_it, title = {AIOne-Agent-52B-A36B-it: A Korean Sparse-MoE Multimodal Model}, author = {JDONE Research}, year = {2026}, howpublished = {\url{https://huggingface.co/JDONE-Research/AIOne-Agent-52B-A36B-it}}}import torchfrom transformers import AutoProcessor, Gemma4ForConditionalGeneration MODEL_ID = "JDONE-Research/AIOne-Agent-52B-A36B-it" model = Gemma4ForConditionalGeneration.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(MODEL_ID) messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image.jpg"}, {"type": "text", "text": "이 사진에 무엇이 보이나요? 한국어로 답해주세요."}, ], },] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True,).to(model.device) generated = model.generate(**inputs, max_new_tokens=256, do_sample=False)print( processor.tokenizer.decode( generated[0, inputs.input_ids.shape[1]:], skip_special_tokens=True ))messages = [ { "role": "user", "content": [ { "type": "text", "text": "사과 3개와 배 5개의 가격이 12,000원입니다. " "사과 1개가 1,500원이라면 배 1개 가격은? 단계적으로 풀이해주세요.", }, ], },]vllm serve JDONE-Research/AIOne-Agent-52B-A36B-it \ --dtype bfloat16 \ --tensor-parallel-size 4 \ --max-model-len 32768@misc{aione_agent_52b_a36b_it, title = {AIOne-Agent-52B-A36B-it: A Korean Sparse-MoE Multimodal Model}, author = {JDONE Research}, year = {2026}, howpublished = {\url{https://huggingface.co/JDONE-Research/AIOne-Agent-52B-A36B-it}}}