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
```

Qwen3.5-KETI-HAECHI-27B / reports /BENCHMARK_GUIDE.md

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Benchmark Guide

Reading the numbers

All percentage deltas in the model report are Tuned minus that model's own official Base, in absolute percentage points. Qwen and HCX raw scores must not be mixed into a single architecture ranking because their official checkpoints, chat templates, vision policies and pretraining histories differ.

Metric	Meaning	Direction	Important caveat
Exact / normalized exact	After evaluator normalization, prediction and canonical answer are identical	Higher	Correct content with extra prose can fail
Containment	Canonical target occurs inside normalized output	Higher	More permissive than exact; hallucinated extra claims may still pass
Format	Required answer wrapper/choice/short-answer form was parsed	Higher	Format success is not semantic correctness
Similarity	Evaluator's normalized string/semantic similarity in [0,1]	Higher	Near-name answers can score partially without being canonical
CER	Character edit distance divided by reference length	Lower	Can exceed 1 when insertion is heavy
Line recall	Fraction of reference OCR lines recovered	Higher	Does not penalize all extra text
Accuracy / aAcc	Correct rows divided by evaluated rows; Hallusion aAcc balances its paired structure	Higher	Compare only matched protocols
pass@1	First generated program passes hidden tests	Higher	Sensitive to code extraction and runtime
ToolSandbox similarity	Milestone completion minus minefield penalties, aggregated by scenario	Higher	Continuous, not plain accuracy
Tau reward	End-to-end task reward from actions, state checks and communication	Higher	Domain/trial weighting must match

Benchmark families

General vision-language

MMBench DEV EN v1.1: English visual perception, relation, logic and knowledge multiple choice.
MMStar / MMStar-KO: leakage-reduced multimodal core skills in English and Korean.
KRETA: Korean-centric document, chart, scene and reasoning evaluation.
MMMU-Pro 10c: college-level multidisciplinary visual problems with ten choices.
HallusionBench: visual faithfulness and hallucination resistance; report aAcc.
MathVista MINI: visual mathematical reasoning. It is available in the HCX comparison; the Qwen run retained predictions but not an authoritative scored row.

Mammoth OCR and heritage

Heritage Multi asks attributes, Reverse selects the matching image, and Simple freely names a heritage object.
OCR Font, Outdoor and Public cover rendered fonts, signs/scenes and administrative documents.
Exact, containment, similarity, CER and line recall should be read together. Exact alone confounds recognition and output policy.

Cultural Focus and H400/HS100

Cultural Focus identity is exact official-title recognition; view caption is strict caption/year matching. overall aggregates both and is therefore much harder than identity alone.
H400 Direct is open-vocabulary held-out naming; H400 Hard is same-family multiple choice; Knowledge Image/Text test factual knowledge.
HS100 Train measures memorization/fit, Unseen measures new-view transfer, and Simple is only the 102-row intersection with the original Simple style. These are not aliases for full Mammoth Heritage Simple (768 rows).

OpenCompass language

Core: IFEval, AIME 2024/2025, PRM800K math, BBH, GPQA, MMLU-Pro, HumanEval, LiveCodeBench and long-context suites.
Extra: ARC, BoolQ, COPA, CommonsenseQA, HellaSwag, OpenBookQA, PIQA, SIQA, WinoGrande, MMLU, GSM8K, MATH, MBPP, LAMBADA, DROP and Natural Questions.
Korean: KMMLU, CSATQA, HAE-RAE, K2-Eval, KoBEST, KOBALT and Korean clinical QA.
The first row in each score table is the configured aggregate, not another dataset.

Tool use and agents

BFCL V4 parses function calls into ASTs and tests simple, multiple, parallel and multi-turn calls, including missing-function/parameter and long-context cases.
ToolSandbox runs phone-like stateful scenarios with distractor tools, milestone goals and minefields.
Tau2 evaluates airline, retail and telecom interactions. Tau3 adds revised environments and banking knowledge with repeated trials.
BFCL's run directory retained score CSVs and diagnostic logs but not the raw generation corpus; paired output examples are therefore provided for Tau2/Tau3, while BFCL is documented with its category scores and observed empty-response diagnostics in the main report.