BIGJUTT/5556/awaxis-31b - Storage Bucket

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))

License

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

Acknowledgements

• TeichAI for the Opus-Distill base
• Google DeepMind for Gemma-4

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Built with Darwin V8 FFN-crossbreed merge engine. Measured numbers above are exact; nothing inflated.