Instructions to use KETI-NLP/Qwen3.5-KETI-HAECHI-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KETI-NLP/Qwen3.5-KETI-HAECHI-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="KETI-NLP/Qwen3.5-KETI-HAECHI-27B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("KETI-NLP/Qwen3.5-KETI-HAECHI-27B")
model = AutoModelForMultimodalLM.from_pretrained("KETI-NLP/Qwen3.5-KETI-HAECHI-27B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KETI-NLP/Qwen3.5-KETI-HAECHI-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KETI-NLP/Qwen3.5-KETI-HAECHI-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KETI-NLP/Qwen3.5-KETI-HAECHI-27B",
		"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/KETI-NLP/Qwen3.5-KETI-HAECHI-27B

SGLang

How to use KETI-NLP/Qwen3.5-KETI-HAECHI-27B 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 "KETI-NLP/Qwen3.5-KETI-HAECHI-27B" \
    --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": "KETI-NLP/Qwen3.5-KETI-HAECHI-27B",
		"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 "KETI-NLP/Qwen3.5-KETI-HAECHI-27B" \
        --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": "KETI-NLP/Qwen3.5-KETI-HAECHI-27B",
		"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 KETI-NLP/Qwen3.5-KETI-HAECHI-27B with Docker Model Runner:
```
docker model run hf.co/KETI-NLP/Qwen3.5-KETI-HAECHI-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.5-KETI-HAECHI-27B

HAECHI personalization with Qwen and HyperCLOVA

Qwen3.5-KETI-HAECHI-27B is a multimodal model derived from Qwen/Qwen3.5-27B. It was developed for two primary purposes: improving Korean cultural-heritage understanding and Korean OCR, and improving tool calling and multi-step, stateful agent execution. Alongside these goals, the model preserves broad multimodal, language, and coding capabilities from the base model.

Core capability profile

Korean cultural heritage and OCR: identifies the official names of heritage objects, answers questions grounded in heritage images, and reads Korean text from signs, scenes, rendered text, and public documents.
Tool calling and long-horizon task execution: selects and calls tools, carries information across multiple turns, tracks changing state, and works toward an end-to-end goal over several steps.
General multimodal understanding: interprets images and text together, follows Korean and English instructions, and performs visual reasoning beyond the specialized heritage domain.

Performance overview

Bar chart comparing the official Qwen3.5-27B base model with Qwen3.5-KETI-HAECHI-27B on representative cultural-heritage, Korean OCR, long-horizon agent, tool-calling, and general multimodal benchmarks

The chart highlights representative metrics from each major capability area. Detailed benchmark tables appear in the Evaluation section.

Quickstart

Qwen3.5 support and AutoModelForMultimodalLM may be unavailable in older Transformers releases. The commands below reproduce the clean environment used to verify this repository. torchvision is required when AutoProcessor initializes the bundled video processor, including for image-only inference.

pip install "torch==2.9.1" "torchvision==0.24.1" accelerate pillow safetensors
pip install "transformers @ git+https://github.com/huggingface/transformers.git@c93057d4835cd31752bb56f59989dd27696eb45b"

When the model files are stored at the root of a Hugging Face repository:

import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "KETI-AIR/Qwen3.5-KETI-HAECHI-27B"
image_url = (
    f"https://huggingface.co/{model_id}/resolve/main/"
    "samples/qualitative/06_cheomseongdae.jpg"
)

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
).eval()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": image_url,
            },
            {
                "type": "text",
                "text": "사진 속 국가유산의 정확한 공식 명칭만 답하세요. 설명은 쓰지 마세요.",
            },
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=False,
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=32,
        do_sample=False,
    )

generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
answer = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
print(answer)

# Example output from this checkpoint:
# 경주 첨성대

For the packaged directory in this release, use model_id = "./model" and image_url = "samples/qualitative/06_cheomseongdae.jpg". For a text-only request, omit the image item and retain only a {"type": "text", "text": ...} item in the message content.

Thinking mode is enabled by default in the bundled chat template. The example disables it for concise answers. Set enable_thinking=True for tasks that benefit from explicit reasoning, and adjust the generation budget accordingly.

Evaluation

All reported deltas are calculated as Qwen3.5-KETI-HAECHI-27B minus the untouched official Qwen/Qwen3.5-27B checkpoint. Percentage deltas are absolute percentage points. Both models used matched API, chat-template, thinking, stop-token, parser, and dataset settings.

Qualitative comparison with the base model

Korean cultural-heritage recognition

Target (reference)	Qwen3.5-27B base	Qwen3.5-KETI-HAECHI-27B
첨성대 (`경주 첨성대`)	❌ `경주 대릉원 석물`	✅ `경주 첨성대`
금동연가7년명여래입상	❌ `금동약사여래입상`	✅ `금동연가7년명여래입상`
백자 철화포도원숭이문 항아리	❌ `분청사기철화포도문호`	✅ `백자 철화포도원숭이문 항아리`
무령왕 금제 관식	❌ `금동관식`	✅ `무령왕 금제 관식`

Korean OCR

Font

Target (reference)	Qwen3.5-27B base	Qwen3.5-KETI-HAECHI-27B
`비서`	❌ `日1人→`	✅ `비서`
`팔월`	❌ `파일`	✅ `팔월`
`떠들다`	❌ `따라들다`	✅ `떠들다`
`벌금`	❌ `별금`	✅ `벌금`

Outdoor

Target (reference)	Qwen3.5-27B base	Qwen3.5-KETI-HAECHI-27B
`아이원 아동발달상담센터`	❌ `아아원` `아동발달상담센터`	✅ `아이원 아동발달상담센터`
`필 노래타운`	❌ `꿀 노래타운`	✅ `필 노래타운`
`써니네호프`	❌ `서니네호프`	✅ `써니네호프`
`못된`	❌ `몬된`	✅ `못된`

Public exec

Target (reference)	Qwen3.5-27B base	Qwen3.5-KETI-HAECHI-27B
`종합토지세`	❌ `홍합회포지` `홍합회포지` `홍합회포지`	✅ `종합토지세`
`영락공원묘지`	❌ `영락공원토지`	✅ `영락공원묘지`
`김해시장(전산정보과장)`	❌ `김해시청(전산정보과장)`	✅ `김해시장(전산정보과장)`

Quantitative benchmarks

Korean cultural-heritage performance

Benchmark	Official base	Qwen3.5-KETI-HAECHI-27B	Delta
H400 direct exact	0.92%	38.07%	+37.16 pp
H400 hard choice	26.45%	80.43%	+53.98 pp
H400 knowledge image	11.45%	25.30%	+13.85 pp
H400 knowledge text	5.97%	6.28%	+0.31 pp

H400 is an internal evaluation set built around 400 Korean cultural-heritage items. It evaluates canonical-name recognition, fine-grained discrimination among visually similar heritage items, and heritage knowledge grounded in either images or text. H400 direct exact measures open-ended canonical-name retrieval, whereas H400 hard is closed-set selection. Their large gap indicates that recognition and discrimination are substantially stronger than exact free-form naming.

Korean (Hangul) OCR performance

Benchmark	Official base	Qwen3.5-KETI-HAECHI-27B	Delta
`ocr_font` exact	19.66%	19.66%	+0.00 pp
`ocr_outdoor` exact	36.13%	41.41%	+5.27 pp
`ocr_public_exec` exact	4.17%	5.21%	+1.04 pp

These Mammoth OCR results use strict exact match, where additional prose or differences in spacing and normalization can cause an otherwise useful answer to be marked incorrect.

Tool calling and long-horizon task performance

Tau2

Benchmark	Official base	Qwen3.5-KETI-HAECHI-27B	Delta
Tau2 airline (n=32)	65.62%	71.88%	+6.25 pp
Tau2 retail (n=32)	56.25%	65.62%	+9.38 pp
Tau2 telecom (n=32)	87.50%	78.12%	-9.38 pp
Tau2 weighted overall (n=96)	69.79%	71.88%	+2.08 pp

Tau2 measures end-to-end success across multi-step airline, retail, and telecom workflows. It improves overall, especially in airline and retail, although the telecom domain declines.

BFCL V4

Benchmark	Official base	Qwen3.5-KETI-HAECHI-27B	Delta
Overall accuracy	32.77%	32.11%	-0.66 pp
Non-Live AST accuracy	89.60%	89.44%	-0.16 pp
Non-Live Simple AST	79.42%	79.25%	-0.17 pp
Non-Live Multiple AST	95.50%	95.50%	+0.00 pp
Non-Live Parallel AST	91.50%	91.00%	-0.50 pp
Non-Live Parallel Multiple AST	92.00%	92.00%	+0.00 pp
Multi-turn accuracy	66.25%	64.50%	-1.75 pp
Multi-turn base	76.50%	76.00%	-0.50 pp
Multi-turn missing function	66.00%	64.00%	-2.00 pp
Multi-turn missing parameter	53.00%	52.00%	-1.00 pp
Multi-turn long context	69.50%	66.00%	-3.50 pp

BFCL V4 measures structured function-call generation and multi-turn recovery from missing functions or parameters. The small overall regression indicates that tool calling and long-horizon performance remain sensitive to the environment and task protocol.

General multimodal and language retention

Benchmark	Official base	Qwen3.5-KETI-HAECHI-27B	Delta
General-VL macro (6)	45.44%	77.60%	+32.16 pp
MMBench DEV EN v1.1	30.42%	90.63%	+60.22 pp
MMStar	39.40%	77.33%	+37.93 pp
MMStar-KO	45.20%	72.33%	+27.13 pp
KRETA	48.54%	86.15%	+37.60 pp
MMMU-Pro 10c	44.28%	61.85%	+17.57 pp
HallusionBench aAcc	64.77%	77.29%	+12.51 pp
OpenCompass Core	63.49%	64.80%	+1.31 pp
OpenCompass Extra	57.26%	57.61%	+0.35 pp
OpenCompass Korean	17.18%	16.77%	-0.41 pp

See the detailed score report for all 148 metrics, the benchmark guide for definitions and caveats, and the machine-readable results for downstream analysis.

Intended use

The model is intended for research and prototyping involving:

Korean cultural-heritage image identification and visual question answering;
Korean scene, sign, font, and public-document OCR;
image-grounded heritage knowledge retrieval;
multi-step, stateful tool-use workflows with explicit monitoring and recovery;
general image understanding and text generation; and
analysis of domain specialization and capability retention in multimodal models.

Responsible use

Verify cultural-property names and factual claims against authoritative catalogs or domain experts. Preserve human review for public descriptions, education, archival metadata, and research outputs. Do not treat model output as evidence of authenticity or provenance. Avoid submitting sensitive or personal documents for OCR unless the deployment provides appropriate privacy controls.

Limitations

OCR outputs may omit text, normalize spelling incorrectly, or add unsupported text.
The model can hallucinate names, dates, designations, provenance, and historical claims. Similar-looking artifacts and uncommon viewpoints are especially challenging.
General multilingual, video, robustness, demographic-bias, privacy, and safety behavior were not comprehensively evaluated for this release.
As with the base model, generated content may be inaccurate, biased, unsafe, or unsuitable for the user's context.

Core contributors

San Kim (kimsan0622@keti.re.kr) — Led long-context agent task performance and development/management of the main training loop.
Byunggill Joe (byunggill@keti.re.kr) — Secured GPU compute resources through the support program and led Korean cultural-heritage recognition/OCR performance.

Acknowledgements

Qwen3.5-KETI-HAECHI-27B was developed using compute resources provided through the 첨단 GPU 활용 지원 사업 of the National IT Industry Promotion Agency (NIPA; 정보통신산업진흥원).

License and attribution

This derivative checkpoint is released under the Apache License 2.0, following the upstream Qwen/Qwen3.5-27B license. Use of third-party data and generated outputs may be subject to additional terms. Please acknowledge the Qwen team and cite the upstream model when using this checkpoint in published work.

Citation

@misc{qwen35_keti_haechi_27b,
  title        = {Qwen3.5-KETI-HAECHI-27B},
  author       = {Kim, San and Joe, Byunggill},
  year         = {2026},
  howpublished = {Hugging Face model repository},
  url          = {https://huggingface.co/KETI-AIR/Qwen3.5-KETI-HAECHI-27B}
}

한국어 요약

이 모델은 Qwen/Qwen3.5-27B를 기반으로 두 가지 목적을 위해 개발한 멀티모달 모델입니다. 첫 번째 목적은 한국 문화유산의 정확한 명칭 식별, 이미지 기반 문화유산 질의응답, 한글 OCR 능력을 향상하는 것입니다. 두 번째 목적은 적절한 도구를 호출하고 여러 단계에 걸쳐 정보를 기억하며 변화하는 상태를 추적하는 장기작업 수행 능력을 향상하는 것입니다.

공식 베이스 모델과 동일한 조건의 내부 평가에서 H400 직접 식별 정확도는 0.92%에서 38.07%로, H400 유사 문화재 선택 정확도는 26.45%에서 80.43%로 향상되었습니다. 장기작업 평가인 Tau2 가중 점수는 69.79%에서 71.88%로 향상되었습니다. 다만 세부 환경에 따라 성능 차이가 있으므로, 실제 에이전트 작업에서는 단계별 상태 확인과 실패 복구 절차를 함께 적용하는 것을 권장합니다.

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