Instructions to use Anserwise/AWAXIS-Think-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anserwise/AWAXIS-Think-31B with Transformers:

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

pipe = pipeline("text-generation", model="Anserwise/AWAXIS-Think-31B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Anserwise/AWAXIS-Think-31B")
model = AutoModelForImageTextToText.from_pretrained("Anserwise/AWAXIS-Think-31B")
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=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-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anserwise/AWAXIS-Think-31B"
# 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-31B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anserwise/AWAXIS-Think-31B

SGLang

How to use Anserwise/AWAXIS-Think-31B 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-31B" \
    --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-31B",
		"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-31B" \
        --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-31B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anserwise/AWAXIS-Think-31B with Docker Model Runner:
```
docker model run hf.co/Anserwise/AWAXIS-Think-31B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AWAXIS-Think-31B

Overview

AWAXIS-Think-31B is a 31B-parameter Korean/English reasoning model created through the VIDRAFT Darwin AI Model Breeding/Evolution Platform. This model was produced using Darwin's proprietary FFN-crossbreed merge engine (V8), which emulates biological crossbreeding between AI models to create offspring with combined strengths of both parents.

AWAXIS-Think-31B은 VIDRAFT Darwin AI 모델 교배/진화 플랫폼을 통해 생성된 31B 파라미터 한국어/영어 추론 모델입니다.

VIDRAFT Darwin Platform

**VIDRAFT Darwin**은 AI 모델의 **교배(Crossbreeding)와 진화(Evolution)**를 통해 새로운 고성능 모델을 자동 생성하는 플랫폼입니다. 생물학적 유전 원리에서 영감을 받아, 두 개 이상의 부모 모델에서 각각의 장점을 선택적으로 결합하여 자식 모델을 탄생시킵니다.

Darwin 교배/진화 핵심 기술

기술	설명
FFN Crossbreed Engine (V8)	부모 모델의 Feed-Forward Network(FFN) 레이어를 선택적으로 교차 결합하는 핵심 엔진. 어텐션·임베딩은 어머니(Mother)에서, FFN 시그널은 아버지(Father)에서 추출하여 블렌딩
Smart MRI (Model Resonance Imaging)	두 모델 간 레이어별 유사도·호환성을 분석하여 최적 교배 비율(alpha)을 자동 탐색하는 기술
Alpha Grid Search	교배 비율 alpha를 체계적으로 탐색(0.1~0.4)하여 벤치마크 성능이 최대화되는 최적점을 발견
Multi-Generation Breeding	1세대 교배 결과물을 다시 부모로 삼아 2세대, 3세대 교배를 수행하는 다세대 진화

Darwin 교배 프로세스 (이 모델의 생성 과정)

[Step 1] 부모 선정 (Parent Selection)
   - Mother: TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2 (추론 능력 기반)
   - Father: google/gemma-4-31B-it (Gemma-4 원본 FFN 기여)

[Step 2] Smart MRI 호환성 분석
   - 두 모델의 60개 레이어별 FFN 텐서 유사도 스캔
   - 아키텍처 호환성 확인 (동일 Gemma-4 family = 100% 호환)

[Step 3] FFN Crossbreed (교배 실행)
   - 어머니의 어텐션, 임베딩, 라우팅 = 100% 보존
   - 아버지의 FFN (gate_proj, up_proj, down_proj) = alpha 비율로 블렌딩
   - 수식: w_child = w_mother * (1 - alpha) + w_father * alpha

[Step 4] Alpha Grid Search (최적 교배 비율 탐색)
   - alpha = {0.1, 0.2, 0.3, 0.4} 4종 생성
   - CLIcK-50 벤치마크로 각 alpha 평가
   - 최적: alpha = 0.1 (CLIcK-200 = 86.0%)

[Step 5] 검증 및 출시
   - GPQA Diamond, CLIcK 등 벤치마크 검증
   - HuggingFace 모델 허브 공개

왜 Darwin 교배인가?

기존 모델 합성 방식(단순 가중치 평균, SLERP, TIES 등)과 달리, Darwin 교배는:

생물학적 유전 모방: 어머니/아버지 역할을 명확히 분리하여 각 부모의 핵심 능력만 선택적으로 상속
FFN 선택적 주입: 어텐션(문맥 이해)은 어머니에서 100% 보존하고, FFN(지식·추론 패턴)만 아버지에서 교차 → 능력 충돌 최소화
벤치마크 기반 자연선택: alpha grid search로 여러 자식 후보를 생성한 뒤, 실측 벤치마크로 최적 개체를 선택 (= 자연선택 시뮬레이션)
다세대 진화 가능: 이 모델(AWAXIS-Think-31B)이 다시 AWAXIS-KR-31B의 아버지가 되어 2세대 교배 수행 → 능력 누적 진화

Build Recipe (Honest Disclosure)

Mother (kept full): TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2 — reasoning-distill base, retained 100% (incl. <think> chain-of-thought style)
Father (FFN donor): google/gemma-4-31B-it — base Gemma-4 FFN tensors blended at alpha = 0.1
Method: Darwin V8 FFN-crossbreed — per-layer FFN blend w = w_mother*(1-alpha) + w_father*alpha on mlp.{gate,up,down}_proj + pre/post_feedforward_layernorm for all 60 language-model layers; grid search alpha in {0.1, 0.2, 0.3, 0.4} on CLIcK-50 — best alpha=0.1 (CLIcK-200 = 86.0%)
Platform: VIDRAFT Darwin AI Model Breeding/Evolution Platform (VIDraft on HuggingFace)
Architecture: Gemma4ForConditionalGeneration (multimodal wrapper; text generation primary)
Tokenizer: Gemma-4 (vocab 262,144)

Model Lineage (Genealogy)

AWAXIS-Think-31B  (this model -- Darwin V8 FFN-crossbreed)
|
+-- Mother (kept full, 100%)
|   TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2
|   -- Claude Opus reasoning distill base
|
+-- Father (FFN donor, alpha=0.1)
    google/gemma-4-31B-it
    -- Gemma-4 base FFN tensors

Common ancestor: Google Gemma-4 architecture.

Measured Benchmarks

Benchmark	Setting	Result
GPQA Diamond 20Q (seed 42)	greedy, max_new_tokens=4096, 2-way DP	12/20 = 60.0% (16/20 still hit token cap, 0 null)
GPQA Diamond 20Q (seed 42)	greedy, max_new_tokens=2048	9/20 = 45.0% (16/20 truncated, 2 null) — truncation artifact, included for transparency
CLIcK (Korean) 200Q	greedy alpha-grid winner	86.0%

Honest Caveats

GPQA 60% is from n=20 (small sample). 16/20 still hit the 4096-token cap — real ceiling may be higher with longer generation budget.
Comparison to random baseline: GPQA random 25% — +35pp clear learning signal.
The full GPQA Diamond (198Q) and other broad suites have not yet been measured for this exact merged artifact.
The model retains the Mother's <think>...</think> reasoning template — strip via post-processing if undesired.

Intended Use

Korean/English step-by-step reasoning, instruction following, knowledge QA
The Think suffix reflects the inherited Opus-distilled chain-of-thought behavior
2nd-generation breeding parent: This model served as the Father for AWAXIS-KR-31B, demonstrating Darwin's multi-generation evolution capability

Out-of-Scope / Limitations

Not a final clinical/legal advisor; outputs may be confidently wrong on hard graduate-level questions
Inherits Gemma-4 base limitations (multimodal wrapper retained; image inputs not the primary use-case here)
Subject to Gemma Terms of Use; see parent model cards for derivative-use clauses

Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("Anserwise/AWAXIS-Think-31B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "Anserwise/AWAXIS-Think-31B",
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="eager",
)

msgs = [{"role": "user", "content": "양자역학의 불확정성 원리를 자세히 설명해 주세요."}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inp = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=2048, do_sample=False)
print(tok.decode(out[0][inp["input_ids"].shape[-1]:], skip_special_tokens=True))

License

Gemma Terms of Use (inherited from base). Use of this model is bound by Google Gemma Terms.

Acknowledgements

VIDRAFT — Darwin AI Model Breeding/Evolution Platform
TeichAI for the Opus-Distill base
Google DeepMind for Gemma-4

Built with the VIDRAFT Darwin AI Model Breeding/Evolution Platform — FFN-crossbreed V8 engine. This model was generated through Darwin's automated crossbreeding process, which selectively combines the strengths of parent models using biologically-inspired genetic algorithms. Measured numbers above are exact; nothing inflated.