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

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
```

AWAXIS-Think-31B

AWAXIS-Think-31B is a 31B-parameter Korean/English reasoning model built via the Darwin V8 FFN-crossbreed merge engine.

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 慣 = 0.1
Method: per-layer FFN blend w = w_mother*(1-慣) + w_father*慣 on mlp.{gate,up,down}_proj + pre/post_feedforward_layernorm for all 60 language-model layers; grid search 慣??0.1, 0.2, 0.3, 0.4} on CLIcK-50 ??best 慣=0.1 (CLIcK-200 = 86.0%)
Architecture: Gemma4ForConditionalGeneration (multimodal wrapper; text generation primary)
Tokenizer: Gemma-4 (vocab 262,144)

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 慣-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

Out-of-scope / limitations

Not a final clinical/legal advisor; outputs may be confidently wrong on hard graduate-level questions (40% wrong on the GPQA-20 set).
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",   # required for the Gemma4 multimodal wrapper
)
msgs = [{"role": "user", "content": "?쒓뎅?대줈 ?먯떊???뚭컻??二쇱꽭??"}]
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))