Instructions to use FINAL-Bench/Darwin-27B-KR-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-27B-KR-V2 with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-27B-KR-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-KR-V2")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-27B-KR-V2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Darwin-27B-KR-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-27B-KR-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-27B-KR-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-27B-KR-V2

SGLang

How to use FINAL-Bench/Darwin-27B-KR-V2 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 "FINAL-Bench/Darwin-27B-KR-V2" \
    --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": "FINAL-Bench/Darwin-27B-KR-V2",
		"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 "FINAL-Bench/Darwin-27B-KR-V2" \
        --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": "FINAL-Bench/Darwin-27B-KR-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-27B-KR-V2 with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-27B-KR-V2
```

Darwin-27B-KR-V2 / README.md

SeaWolf-AI

Darwin-27B-KR-V2: Korean SFT on Darwin-27B-KR (BF16, 1027 pairs, LoRA r=64)

427487f verified 4 months ago

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	---
	license: apache-2.0
	base_model:
	- FINAL-Bench/Darwin-27B-KR
	tags:
	- qwen3.5
	- korean
	- sft
	- reasoning
	- thinking
	- darwin
	- k-ai
	language:
	- ko
	- en
	- ja
	- zh
	- multilingual
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Darwin-27B-KR-V2

	> Qwen3.5 Hybrid Architecture \| ~26B Params \| Thinking Mode \| 262K Context \| BF16 \| Apache 2.0

	---

	## Model Overview

	Darwin-27B-KR-V2 is the next evolution of [FINAL-Bench/Darwin-27B-KR](https://huggingface.co/FINAL-Bench/Darwin-27B-KR), enhanced with targeted Korean SFT (Supervised Fine-Tuning) for K-AI Leaderboard optimization.

	Built on VIDRAFT's Darwin evolutionary merge lineage, this model inherits strong chain-of-thought reasoning from Darwin-27B-Opus and further refines Korean language capabilities through carefully curated training data targeting MuSR (Multi-Step Reasoning), KMMLU-Pro (Korean domain knowledge), and Metacognitive evaluation.

	### Key Features

	- Darwin lineage — Evolutionary merge backbone from VIDRAFT's Darwin-27B-Opus
	- K-AI targeted SFT — 1,027 curated Korean reasoning & knowledge pairs
	- Thinking mode — `<think>` tag based step-by-step reasoning
	- 262K context — Ultra-long document processing
	- BF16 — Memory-efficient (~48GB)
	- Apache 2.0 — Free for commercial use

	---

	## Training

	\| Item \| Details \|
	\|---\|---\|
	\| Base Model \| [FINAL-Bench/Darwin-27B-KR](https://huggingface.co/FINAL-Bench/Darwin-27B-KR) \|
	\| Method \| LoRA SFT (rank=64, alpha=128) + Merge \|
	\| Data \| 1,027 Korean SFT pairs (MuSR 428 + KMMLU-Pro 500 + Metacognitive 99) \|
	\| Epochs \| 2 \|
	\| Learning Rate \| 2e-5 (cosine schedule) \|
	\| Effective Batch \| 16 \|
	\| Target Modules \| q/k/v/o_proj, gate/up/down_proj (1.17% trainable) \|
	\| Hardware \| 8x NVIDIA B200 (183GB each) \|
	\| Training Time \| ~25 minutes \|
	\| Final Loss \| 0.66 \|
	\| Precision \| BF16 \|

	### SFT Data Composition

	\| Source \| Count \| Description \|
	\|---\|---\|---\|
	\| MuSR (Korean) \| 428 \| Multi-step reasoning: causal, temporal, spatial, counterfactual \|
	\| KMMLU-Pro \| 500 \| Korean domain knowledge: law, economics, science, history, medicine \|
	\| Metacognitive \| 99 \| Self-correcting reasoning with TICOS framework \|
	\| Total \| 1,027 \| All pairs include `<think>` reasoning tags \|

	---

	## Model Specifications

	\| Property \| Value \|
	\|---\|---\|
	\| Architecture \| Qwen3.5 (GatedDeltaNet Hybrid Attention, 64-layer) \|
	\| Parameters \| ~26B \|
	\| Hidden Size \| 5120 \|
	\| Layers \| 64 \|
	\| Context Length \| 262,144 tokens \|
	\| Precision \| BF16 (~48GB) \|
	\| Vocab Size \| 248,320 \|
	\| Thinking \| Supported (`<think>` tags) \|
	\| License \| Apache 2.0 \|

	---

	## VRAM Requirements

	\| Setup \| VRAM \| Notes \|
	\|---\|---\|---\|
	\| BF16 (native) \| ~48 GB \| Single H100/B200 or 2x A100 \|
	\| 4-bit quantized \| ~14 GB \| Single RTX 4090 \|
	\| 8-bit quantized \| ~26 GB \| Single A6000 \|

	---

	## Usage

	> Requirements: `transformers >= 4.57.0`

	### Transformers

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-KR-V2")
	model = AutoModelForCausalLM.from_pretrained(
	"FINAL-Bench/Darwin-27B-KR-V2",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)

	messages = [{"role": "user", "content": "대한민국 헌법재판소의 역할과 권한에 대해 설명해주세요."}]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
	print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
	```

	### vLLM

	```bash
	vllm serve FINAL-Bench/Darwin-27B-KR-V2 \
	--enforce-eager \
	--max-model-len 32768 \
	--dtype bfloat16
	```

	---

	## Lineage

	```
	Qwen/Qwen3.5-27B
	\|
	v
	FINAL-Bench/Darwin-27B-Opus (evolutionary merge by VIDRAFT)
	\|
	v
	FINAL-Bench/Darwin-27B-KR (Korean-specialized variant)
	\|
	v
	FINAL-Bench/Darwin-27B-KR-V2 (this model, + K-AI targeted SFT)
	```

	---

	## Acknowledgements

	- [VIDRAFT / FINAL-Bench](https://huggingface.co/FINAL-Bench) — Darwin evolutionary merge system
	- [Qwen Team](https://huggingface.co/Qwen) — Qwen3.5 architecture

	---

	## Citation

	```bibtex
	@misc{darwin_27b_kr_v2_2026,
	title = {Darwin-27B-KR-V2: Korean-Enhanced Reasoning Model},
	author = {VIDRAFT},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V2}}
	}
	```