Instructions to use Anserwise/AWAXIS-Think-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anserwise/AWAXIS-Think-27b with Transformers:

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

pipe = pipeline("text-generation", model="Anserwise/AWAXIS-Think-27b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Anserwise/AWAXIS-Think-27b")
model = AutoModelForMultimodalLM.from_pretrained("Anserwise/AWAXIS-Think-27b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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 Anserwise/AWAXIS-Think-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anserwise/AWAXIS-Think-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": "Anserwise/AWAXIS-Think-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anserwise/AWAXIS-Think-27b

SGLang

How to use Anserwise/AWAXIS-Think-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 "Anserwise/AWAXIS-Think-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": "Anserwise/AWAXIS-Think-27b",
		"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 "Anserwise/AWAXIS-Think-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": "Anserwise/AWAXIS-Think-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anserwise/AWAXIS-Think-27b with Docker Model Runner:
```
docker model run hf.co/Anserwise/AWAXIS-Think-27b
```

AWAXIS-Think-27b / README.md

Anserwise

Update transformers requirement to >=5.5.4 to fix qwen3_5_text loading error

dff8872 verified about 2 months ago

preview code

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metadata

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

AWAXIS-Think-27b

FINAL-Bench/Darwin-27B-Opus 기반, 한국어 특화 고품질 SFT를 수행한 추론 모델입니다.

⚠️ Requirements / Loading 주의 이 모델은 model_type: qwen3_5_text (Qwen3.5 하이브리드 아키텍처)를 사용합니다. transformers >= 5.5.4 이상 에서만 정상 로드됩니다.
pip install --upgrade "transformers>=5.5.4"
# 또는 최신 개발판
pip install "transformers @ git+https://github.com/huggingface/transformers.git@main"
구버전 transformers에서 나타나는 model_type 'qwen3_5_text'를 인식하지 못함 오류는 라이브러리 미업데이트로 인한 것이며, 위 명령으로 해결됩니다.

Method

Base Model: Darwin-27B-Opus (Qwen3.5-27B family)
Korean SFT: 한국어 문화, 역사, 법률, 경제, 사회, 지리 등 한국 특화 지식 중심의 고품질 instruction 데이터로 Supervised Fine-Tuning 수행
Thinking Mode: <think> 태그를 통한 Chain-of-Thought 단계적 추론 지원

Benchmark

Benchmark	Score
CLIcK (Korean Cultural & Linguistic Knowledge)	81.0%
KMMLU-Pro (Korean MMLU Professional)	74.0%

Model Specifications

Property	Value
Architecture	Qwen3.5 Hybrid (GatedDeltaNet + Attention, 64 layers)
Parameters	~27B
Hidden Size	5120
Intermediate Size	16384
Context Length	262,144 tokens
Precision	BF16
Vocab Size	248,320
Thinking	Supported (`<think>` tags)
License	Apache 2.0

Usage

Requirements: transformers >= 5.5.4

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Anserwise/AWAXIS-Think-27b",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Anserwise/AWAXIS-Think-27b")

messages = [{"role": "user", "content": "조선시대의 과거제도에 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=1024, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

vLLM

vllm serve Anserwise/AWAXIS-Think-27b \
    --enforce-eager \
    --max-model-len 32768 \
    --dtype bfloat16

Features

Darwin-27B-Opus의 강력한 추론 능력 계승
한국어 문화, 역사, 법률, 경제, 사회 등 한국 특화 지식 강화
Thinking mode를 통한 단계적 사고 추론
다국어 지원 (한국어, 영어, 일본어, 중국어)
262K 컨텍스트 길이 지원

Training

Item	Details
Base Model	FINAL-Bench/Darwin-27B-Opus
Method	Korean-specialized Supervised Fine-Tuning
Data	한국어 문화·지식 중심 고품질 instruction 데이터
Developer	Anserwise

Acknowledgements

FINAL-Bench — Darwin-27B-Opus base model
Qwen Team — Qwen3.5 architecture