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, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("Anserwise/AWAXIS-Think-27b")
model = AutoModelForCausalLM.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
```

Anserwise commited on Apr 15

Commit

11d6c91

verified ·

1 Parent(s): a1c5518

Update README

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README.md +88 -2

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@@ -11,9 +11,95 @@ tags:
 - korean
 - reasoning
 - thinking
 pipeline_tag: text-generation
 ---
-# AWAXIS-Think-27b-v2
-A Korean-optimized 27B reasoning model.

 - 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](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus) 기반, 한국어 특화 고품질 SFT를 수행한 추론 모델입니다.
+## Method
+- **Base Model**: [Darwin-27B-Opus](https://huggingface.co/FINAL-Bench/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 >= 4.57.0`
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "Anserwise/AWAXIS-Think-27b",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+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
+```bash
+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](https://huggingface.co/FINAL-Bench/Darwin-27B-Opus) |
+| **Method** | Korean-specialized Supervised Fine-Tuning |
+| **Data** | 한국어 문화·지식 중심 고품질 instruction 데이터 |
+| **Developer** | [Anserwise](https://huggingface.co/Anserwise) |
+## Acknowledgements
+- [FINAL-Bench](https://huggingface.co/FINAL-Bench) — Darwin-27B-Opus base model
+- [Qwen Team](https://huggingface.co/Qwen) — Qwen3.5 architecture