internvl3_5-1b-balanced

yujuyeon/internvl3_5-1b-balanced

internvl3_5-1b-balanced is a image-text-to-text model from yujuyeon. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

OpenGVLab/InternVL35-1B-Pretrained 를 한국어 멀티모달 데이터로 파인튜닝한 InternVL3.5 specialist.

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8.7 GB on disk

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Hugging Face

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At a glance

Task: Image-Text-to-Text
Library: transformers
License: other
Model type: internvl_chat
Access: Public
Created: Jun 20, 2026
Updated: Jun 20, 2026
SHA: 2d0936c8

Base models

OpenGVLab/InternVL3_5-1B-Pretrained

Model card

From the Hugging Face model README

internvl3_5-1b-balanced

OpenGVLab/InternVL3_5-1B-Pretrained 를 한국어 멀티모달 데이터로 파인튜닝한 InternVL3.5 specialist.

항목	값
Base model	`OpenGVLab/InternVL3_5-1B-Pretrained`
Method	Full
Domain	한:영 1:1 균형

Hyperparameters

num_train_epochs: 1
steps: 5162 / max 5162
train_batch_size: 8
peak learning_rate: 9.999984252764371e-05

Training Loss

init 1.4746 → final 0.9904 (min 0.9242)

step	loss
10	1.4746
520	1.0770
1030	1.0217
1540	1.0139
2050	0.9842
2560	0.9938
3070	0.9712
3580	0.9627
4090	0.9475
4600	0.9431
5110	0.9660
5160	0.9904

Training Data

구성: 40개 서브셋 (한국어 specialist SFT)

subset	repeat
`hf_korLlava_Caption`	2.7
`kr_recap_caption`	1.2857
`chart2table_en`	0.0475
`chartRqa1`	0.3167
`chartRqa2`	0.475
`dvqa_en`	0.0475
`en_chartqa_chart`	0.5278
`en_figureqa_chart`	0.475
`en_mapqa_chart`	1.3571
`kisti_documen_Reason`	2.25
`en_imgtext_doc`	0.5625
`en_vwi_doc`	0.9
`out_kor_llava`	1.9501
`en_allava_general`	3.0
`aihub_mathMultiple`	0.8449
`aihub_mathSubjective`	1.7883
`en_geoqa_math`	1.0
`en_hme_formula`	1.6667
`en_iconqa_math`	0.6667
`en_mavis_math`	0.3333
`hf_latexUpdate`	0.6667
`aihub_subjectTxt_OCR`	0.625
`aihub_visual_OCR`	0.8333
`kisti_arxiv_OCR`	0.8333
`kopub_SDSKoPub_OCR`	0.3077
`en_chrome_ocr`	1.4205
`en_iam_ocr`	2.2093
`en_llavar_ocr`	0.6316
`en_ocrvqa_ocr`	0.25
`aihub_visual_ShortQA`	0.7167
`kisti_hanbat_Reason`	0.7167
`kisti_hanbat_Vqa`	0.86
`en_aokvqa_general`	1.3438
`aihub_subjectImg_Parse`	1.35
`en_tabmwp_table`	0.6136
`en_tatqa_table_text`	1.0385
`tableVqa_Caption`	0.675
`tableVqa_Reason`	0.675
`ko_alpaca_textonly`	0.9006
`en_evol_textonly`	0.3152

Usage

from transformers import AutoModel, AutoTokenizer
import torch
m = AutoModel.from_pretrained("yujuyeon/internvl3_5-1b-balanced", torch_dtype=torch.bfloat16,
                              trust_remote_code=True).eval().cuda()
tok = AutoTokenizer.from_pretrained("yujuyeon/internvl3_5-1b-balanced", trust_remote_code=True, use_fast=False)

Task: Image-Text-to-Text
Library: transformers
Type: internvl_chat
License: other
Languages: ko, en
Created: Jun 20, 2026
Updated: Jun 20, 2026