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

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, 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
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

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

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 — Darwin evolutionary merge system
Qwen Team — Qwen3.5 architecture

Citation

@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}}
}

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for FINAL-Bench/Darwin-27B-KR-V2

Base model

FINAL-Bench/Darwin-27B-Opus

Finetuned

FINAL-Bench/Darwin-27B-KR

Finetuned

(2)

this model