Instructions to use ThakiCloud/Qwen3.8-27B-Human-KO-Finance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThakiCloud/Qwen3.8-27B-Human-KO-Finance with Transformers:

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

pipe = pipeline("text-generation", model="ThakiCloud/Qwen3.8-27B-Human-KO-Finance")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ThakiCloud/Qwen3.8-27B-Human-KO-Finance")
model = AutoModelForMultimodalLM.from_pretrained("ThakiCloud/Qwen3.8-27B-Human-KO-Finance", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThakiCloud/Qwen3.8-27B-Human-KO-Finance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThakiCloud/Qwen3.8-27B-Human-KO-Finance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThakiCloud/Qwen3.8-27B-Human-KO-Finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThakiCloud/Qwen3.8-27B-Human-KO-Finance

SGLang

How to use ThakiCloud/Qwen3.8-27B-Human-KO-Finance 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 "ThakiCloud/Qwen3.8-27B-Human-KO-Finance" \
    --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": "ThakiCloud/Qwen3.8-27B-Human-KO-Finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ThakiCloud/Qwen3.8-27B-Human-KO-Finance" \
        --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": "ThakiCloud/Qwen3.8-27B-Human-KO-Finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThakiCloud/Qwen3.8-27B-Human-KO-Finance with Docker Model Runner:
```
docker model run hf.co/ThakiCloud/Qwen3.8-27B-Human-KO-Finance
```

Human-KO Finance FIN5 — Securities Compliance Advisory Model (with gate)

A 27B model aligned to observe mandatory disclosures and the suitability principle in Korean securities client advisory conversations, without needing prompt instructions to do so. ⛔ The weights must not be used alone — the figures below only hold when this repository's gate is attached alongside them.

Why the gate is mandatory

	Weights alone	With gate
Disclosure coverage	0.905	1.000
Factual errors (out of 200)	0-1	0

Deploying without the gate misses disclosures. Because probabilistic generation cannot guarantee zero, the design has a deterministic verifier block it. Use gates/ together with the model.

Measurements (200-item eval set, median of 3 repetitions)

Metric	FIN2 (volume increase only)	FIN5
Suitability behavior pass	0.08	0.818
Factual errors (raw)	3	0
Disclosure coverage	0.885	0.905
Suitability-refusal rate (over-refusal)	0.008	0.044
Bullets/bold/headers	0	0

Superiority over the baseline was confirmed with the same-item paired McNemar test, all 3 runs p<0.005.

⛔ Limitations (please read these as they are)

Only n=33 items are judgeable for suitability, so the 3-run spread is 0.212. Do not cite the absolute value; judge only by the paired comparison. ±0.05 precision requires n≈380.
The situation classifier's accuracy is 0.885. A misclassification drops a required disclosure from the list, so this repository uses the union of input classification and output-based judging.
Factual errors run 0-1 per 200 cases, so the "0" verdict itself has a statistical-power limit.
⛔ This model does not by itself prevent missing disclosures. The gate only catches shortfalls and backfills them. Legal review is required before deployment into an actual regulatory environment.

Structure

gates/      Disclosure, suitability, output-based, and factual judging and enforcement (7 types)
eval/       Measurement harness + paired superiority test
promptset/  200-item eval set (synthetic)

Source

The evaluation criteria are based on the Korea Financial Investment Association's Standard Investment Recommendation Guidelines and Article 19 of the Financial Consumer Protection Act. ⛔ The Association's original text is copyrighted (ALL RIGHTS RESERVED) and is not included in this repository. Check it directly at https://law.kofia.or.kr.

Lineage

Qwen/Qwen3.8-27B (Apache-2.0) → ThakiCloud/Qwen3.8-27B-Human-KO (Apache-2.0) → FIN5. The training corpus is synthetic data generated by our own serving model.

Paper

Off-Target Effects of Response-Style Alignment in a Korean 27B Language Model — arXiv:2609.11291

This checkpoint is the securities-compliance arm of that study. The paper reports what the compliance and safety stages cost each other depending on the order they are applied, and why the disclosure and abstention numbers here should be read as an emission policy result — how often the model answers and how much it says — not only as a content result.