Instructions to use yujuyeon/internvl3_5-1b-korean-347k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yujuyeon/internvl3_5-1b-korean-347k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="yujuyeon/internvl3_5-1b-korean-347k", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("yujuyeon/internvl3_5-1b-korean-347k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yujuyeon/internvl3_5-1b-korean-347k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yujuyeon/internvl3_5-1b-korean-347k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujuyeon/internvl3_5-1b-korean-347k",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/yujuyeon/internvl3_5-1b-korean-347k

SGLang

How to use yujuyeon/internvl3_5-1b-korean-347k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yujuyeon/internvl3_5-1b-korean-347k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujuyeon/internvl3_5-1b-korean-347k",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yujuyeon/internvl3_5-1b-korean-347k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujuyeon/internvl3_5-1b-korean-347k",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use yujuyeon/internvl3_5-1b-korean-347k with Docker Model Runner:
```
docker model run hf.co/yujuyeon/internvl3_5-1b-korean-347k
```

internvl3_5-1b-korean-347k / README.md

yujuyeon

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metadata

license: other
base_model: OpenGVLab/InternVL3_5-1B
language:
  - ko
  - en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - internvl
  - mllm
  - korean
  - vision-language

internvl3_5-1b-korean-347k

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

항목	값
Base model	`OpenGVLab/InternVL3_5-1B`
Method	Full FT
Domain	한국어 종합 347k

Hyperparameters

num_train_epochs: 5
steps: 10000 / max 13575
train_batch_size: 2
peak learning_rate: 3.999999772287927e-05

Training Loss

init 1.5136 → final 0.3429 (min 0.3178)

step	loss
10	1.5136
1010	0.7029
2010	0.6294
3010	0.5353
4010	0.4988
5010	0.5090
6010	0.4190
7010	0.4065
8010	0.3964
9010	0.3448
10000	0.3429

Training Data

구성: 18개 서브셋 (한국어 specialist SFT)

subset	repeat
`aihub_visual_ShortQA_30k`	1
`hf_korLlava_Caption_20k`	1
`llava_ko_recap_30k`	1
`out_kor_llava_20k`	1
`chartRqa1_30k`	1
`chartRqa2_20k`	1
`tableVqa_Reason_20k`	1
`tableVqa_Caption_20k`	1
`aihub_subjectTxt_OCR_20k`	1
`aihub_visual_OCR_15k`	1
`kisti_arxiv_OCR_15k`	1
`kisti_hanbat_Reason_30k`	1
`kisti_documen_Reason_10k`	1
`aihub_mathMultiple_kor_M0`	1
`aihub_mathSubjective_kor_M0`	1
`kisti_hanbat_Vqa_25k`	1
`hf_latexUpdate_15k`	1
`aihub_subjectImg_Parse_10k`	1

Usage

from transformers import AutoModel, AutoTokenizer
import torch
m = AutoModel.from_pretrained("yujuyeon/internvl3_5-1b-korean-347k", torch_dtype=torch.bfloat16,
                              trust_remote_code=True).eval().cuda()
tok = AutoTokenizer.from_pretrained("yujuyeon/internvl3_5-1b-korean-347k", trust_remote_code=True, use_fast=False)