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

Libraries

How to use yujuyeon/internvl3_5-1b-balanced 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-balanced", 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-balanced", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yujuyeon/internvl3_5-1b-balanced 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-balanced"
# 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-balanced",
		"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-balanced

SGLang

How to use yujuyeon/internvl3_5-1b-balanced 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-balanced" \
    --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-balanced",
		"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-balanced" \
        --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-balanced",
		"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-balanced with Docker Model Runner:
```
docker model run hf.co/yujuyeon/internvl3_5-1b-balanced
```

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)

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for yujuyeon/internvl3_5-1b-balanced

Base model

OpenGVLab/InternVL3_5-1B-Pretrained

Finetuned

(4)

this model