FINAL-Bench / Darwin-60B-DUO

Darwin-60B-DUO

The Darwin family's first DUO model — two domain-verified SOTA models served as a single OpenAI-compatible endpoint.

Darwin-60B-DUO unifies two specialist models from the Darwin family behind a single API:

• Darwin-28B-REASON — Hugging Face leaderboard GPQA Diamond rank #3 , English graduate-level reasoning specialist.
• AWAXIS-Think-31B — National K-AI Leaderboard rank #1 (operated by the Ministry of Science and ICT of the Republic of Korea), Korean specialist.

A Hybrid-A router automatically dispatches each request to the optimal strategy (single route / sequential collaboration / ensemble), so callers see one model and one endpoint while internally benefiting from both specialists.

---

Model Description

Darwin-60B-DUO is a gateway-orchestrated aggregate of two constituent base models. The repository contains a FastAPI orchestrator, configuration, and Docker Compose recipe. The model weights themselves live in the constituent repositories and are loaded at runtime by two vLLM backends.

Note on AWAXIS membership. AWAXIS-Think-31B is also part of the Darwin family — it is the Korean specialist branch distilled by the Darwin team on top of Google's Gemma-4 base, complementing the original Qwen3.5-line Darwin lineage as the family's second axis.

Hybrid-A Orchestration

The gateway analyzes each incoming request and selects one of five strategies. The default distribution observed on representative traffic is:

Average effective cost is approximately 1.3× a single 30 B model — 70 % of traffic is served by a single backend; the remaining 30 % uses both.

---

Intended Use

Primary use cases

• Bilingual Korean-English assistants that require both Korean fluency and high-quality English reasoning.
• Single-endpoint integration where downstream tooling already targets the OpenAI Chat Completions API (LangChain, LlamaIndex, OpenAI SDK, Continue, Cursor, etc.).
• Cost-conscious deployment — most traffic is served by a single backend at 1× cost while difficult cross-domain queries automatically receive a 2× collaboration.

Out-of-scope

• Vision / video generation. Both constituent models are text-mode only as deployed here ( --limit-mm-per-prompt {"image":0,"video":0} ).
• Real-time streaming. Initial gateway release does not stream token-by-token. Streaming is planned for v1.1.
• Direct AutoModel.from_pretrained() loading. This repository contains an orchestrator, not unified weights. Use the gateway or Docker Compose.
• Safety-critical decision making (medical diagnosis, legal advice, autonomous control). LLM hallucinations apply.

---

How to Use

Option A — Docker Compose (recommended)

git clone https://huggingface.co/FINAL-Bench/Darwin-60B-DUO
cd Darwin-60B-DUO

# HF token is needed only to download constituent weights on first launch
export HF_TOKEN=hf_xxx
docker compose -f docker/docker-compose.yml up -d

# Verify
curl http://localhost:8000/v1/models
# {"object":"list","data":[{"id":"darwin-60b-duo",...}]}

Option B — Manual launch (2 × B200 / H100, FP8)

# 1. Darwin-28B-REASON on GPU 0
CUDA_VISIBLE_DEVICES=0 VLLM_DP_MASTER_PORT=45011 \
  vllm serve FINAL-Bench/Darwin-28B-REASON \
    --port 8021 --served-model-name darwin-28r \
    --quantization fp8 --enforce-eager \
    --limit-mm-per-prompt '{"image":0,"video":0}' &

# 2. AWAXIS-Think-31B on GPU 1
CUDA_VISIBLE_DEVICES=1 VLLM_DP_MASTER_PORT=45012 \
  vllm serve Anserwise/AWAXIS-Think-31B \
    --port 8022 --served-model-name awaxis-31b \
    --quantization fp8 --enforce-eager \
    --limit-mm-per-prompt '{"image":0,"video":0}' &

# 3. Gateway
pip install -r gateway/requirements.txt
python gateway/server.py --port 8000 \
    --darwin-url http://127.0.0.1:8021/v1 \
    --awaxis-url http://127.0.0.1:8022/v1

Single-GPU collocation. With FP8 quantization the combined footprint is ~30 GB. Set both backends to CUDA_VISIBLE_DEVICES=0 and --gpu-memory-utilization 0.45 to colocate on a single 80 GB B200 / H100.

OpenAI-compatible call

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="anything")

# The router picks split_refine (Korean output with English/STEM reasoning)
resp = client.chat.completions.create(
    model="darwin-60b-duo",
    messages=[{
        "role": "user",
        "content": "Explain the practical difference between GPT-5 and Claude's reasoning, in Korean.",
    }],
)
print(resp.choices[0].message.content)
# Darwin produces the English reasoning; AWAXIS polishes it into natural Korean.

You can also force a specific strategy via the non-standard duo_strategy field:

resp = client.chat.completions.create(
    model="darwin-60b-duo",
    messages=[{"role":"user","content":"Which is correct? (A) ... (B) ..."}],
    extra_body={"duo_strategy": "ensemble_v1"},  # force MAJ@8 + cross-verify
)

Inspect the chosen strategy in the response under _duo_route :

{
  "choices": [{"message": {"role":"assistant", "content":"..."}}],
  "_duo_route": {
    "strategy": "split_refine",
    "reason": "korean_output_with_english_reasoning",
    "elapsed_s": 4.83,
    "language_ratio": 0.42
  }
}

---

Darwin Family

Darwin-60B-DUO sits at the confluence of two complete lineages — the Qwen3.5-based Darwin lineage (English reasoning) and the Gemma-4-based Darwin Korean-specialist branch (AWAXIS) . The full family tree, with both constituent ancestries fully expanded:

graph TD
    %% Lineage A — English reasoning
    A1[Cohere Command A+ - 218B foundation]:::found --> A2[Darwin-28B-Opus - English reasoning base]:::parent
    A2 --> A3[Darwin-28B-REASON - HF GPQA Diamond #3]:::spec

    %% Other Darwin parents
    A1 -.-> P1[Darwin-218B-Delphi - cascade flagship GPQA 90.91%]:::parent
    A2 -.-> P2[Darwin-9B - omni-modal ko/en compact]:::parent
    P2 -.-> P3[Darwin-31B-Opus - Korean multimodal base]:::parent

    %% Lineage B — Korean specialist
    B1[Google Gemma-4-31B-it - Korean/multilingual base]:::found --> B2[TeichAI gemma-4-31B-it-Claude-Opus-Distill-v2]:::parent
    B2 --> B3[AWAXIS-Think-31B - National K-AI Leaderboard #1, Darwin family Korean specialist]:::spec

    %% The DUO unification
    A3 --> DUO[Darwin-60B-DUO - this model]:::duo
    B3 --> DUO

    classDef found fill:#e8f0fe,stroke:#1a73e8,color:#0a0a0a
    classDef parent fill:#fff4e5,stroke:#f29900,color:#0a0a0a
    classDef spec fill:#e6f4ea,stroke:#34a853,color:#0a0a0a
    classDef duo fill:#fce8f3,stroke:#d81b60,color:#0a0a0a,stroke-width:3px

Plain-text fallback

Darwin Family

Lineage A — English reasoning (Qwen3.5-line)
└── Cohere Command A+ (218B foundation)
    └── Darwin-28B-Opus (English reasoning base)
        └── Darwin-28B-REASON  🥉  ← HF GPQA Diamond #3
                                     (English reasoning specialist)
    │
    │   Related Darwin parents in this lineage:
    │   • Darwin-218B-Delphi  (cascade flagship, GPQA Diamond 90.91 %)
    │   • Darwin-9B           (omni-modal ko/en compact)
    │   • Darwin-31B-Opus     (Korean multimodal base)

Lineage B — Korean specialist (Gemma-4-line)
└── Google Gemma-4-31B-it (Korean / multilingual base)
    └── TeichAI gemma-4-31B-it-Claude-Opus-Distill-v2
        └── AWAXIS-Think-31B  🥇  ← National K-AI Leaderboard #1
                                     (Darwin family Korean specialist)

         │                             │
         └──────── DUO unification ────┘
                          ↓
              ⭐ Darwin-60B-DUO ⭐  ← THIS MODEL
              "Two SOTAs, one OpenAI-compatible endpoint."

The HF Model tree widget (right sidebar) automatically renders the upstream chain from each base_model declared in the YAML frontmatter, so the full ancestry — Cohere Command A+ and Google Gemma-4-31B-it at the roots — is browsable directly on this page.

---

Operation Modes

Mode 1 — Route (single backend, ~70 % of traffic)

The router selects one backend based on language ratio and lightweight keyword heuristics:

• korean_ratio(prompt) > 0.3 → AWAXIS
• ASCII / code / math markers ( def , import , \boxed , prove , …) → Darwin
• Mixed → AWAXIS (Korean-first default)

Mode 2 — Split / Refine (sequential collaboration, ~20 % of traffic)

One model drafts, the other polishes. The polish instruction is language-adaptive:

User: "Explain entropy intuitively in Korean."

Step 1 — Darwin (rigorous English reasoning)
        "Entropy quantifies the number of microstates compatible
         with a given macrostate, representing disorder ..."

Step 2 — AWAXIS (natural Korean polish)
        "엔트로피는 쉽게 말하면 '무질서함의 정도'입니다.
         같은 모습으로 보이지만 사실 그 안에 ..."

The reverse path ( AWAXIS draft → Darwin polish ) fires when the output language is English but the prompt requires Korean cultural or linguistic context.

Mode 3 — Ensemble V₁ Tournament (~10 % of traffic)

For multiple-choice and short-answer queries, both backends produce N = 8 samples at temperature 0.7. Each backend's answer is its own majority vote (self-consistency). If the two majorities agree → return. If they disagree → each backend verifies the pair (cross-verification), and the tournament winner is selected. A confidence tiebreaker (own-vote count) resolves split verdicts.

---

Repository Layout

Darwin-60B-DUO/
├── README.md                  ← this model card
├── config.json                ← DUO configuration & orchestration metadata
├── tokenizer_info.json        ← constituent tokenizer references
├── LICENSE                    ← Gemma + Apache-2.0 dual notice
├── gateway/
│   ├── server.py              ← FastAPI OpenAI-compatible orchestrator
│   ├── router.py              ← language / domain / MCQ classifier
│   ├── refine.py              ← sequential refine (drafter → polisher)
│   ├── ensemble.py            ← V₁ MAJ@N + cross-verification
│   └── requirements.txt
├── docker/
│   └── docker-compose.yml     ← vLLM ×2 + gateway integrated launcher
└── benchmarks/
    └── README.md              ← evaluation roadmap (results TBA)

---

Evaluation

Verified constituent scores

Darwin-60B-DUO aggregate

Aggregate DUO benchmark results will be published in benchmarks/ after formal evaluation. The verified constituent ranks above are independent third-party measurements and are not aggregate DUO scores.

Cost / latency notes

• Route mode: comparable to a single 30 B FP8 backend (1× latency, 1× compute).
• Split mode: ~2× latency (two sequential generations).
• Ensemble V₁: ~2× compute (parallel) plus a short cross-verify round when majorities disagree.

---

Bias, Risks, and Limitations

• Hallucination. Standard LLM caveats apply. Both backends can produce confident but incorrect outputs, especially on out-of-distribution queries.
• Disagreement bias. Empirically, the V₁ tournament occasionally selects a wrong answer that both backends collectively favor over a single backend's correct one. The gateway exposes the routing decision in _duo_route for auditability.
• Language coverage. Best performance on English and Korean. Other languages fall back to the closer-fit backend without explicit optimization.
• Combined weights are not bundled. The aggregator pulls each backend's weights from the constituent repositories. Network and disk for both is required.
• Two-GPU baseline. BF16 deployment requires two GPUs. FP8 quantization enables single-GPU operation on B200 / H100 (80 GB).
• Training data cut-off. Darwin-28B-REASON: ~ 2026-Q1. AWAXIS-Think-31B: ~ 2026-Q1.

---

License

Darwin-60B-DUO inherits the Gemma Terms of Use as its effective combined license — the more restrictive of the two constituent licenses.

The orchestrator code ( gateway/ , docker/ ) is offered under Apache-2.0 to maximize developer flexibility; combined-license inheritance applies to served model behavior only.

Please review the Gemma Terms of Use and the Gemma Prohibited Use Policy before commercial deployment.

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Acknowledgments

• FINAL-Bench team — Darwin family architecture and DUO concept
• Anserwise Korean specialist team — AWAXIS-Think-31B development
• VIDRAFT — orchestration framework and the Hybrid-A routing strategy
• Google DeepMind — Gemma-4 foundation
• Cohere and Qwen teams — Command A+ / Qwen3.5 foundation lineage

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Contact

• Hugging Face org: FINAL-Bench
• Sister orgs: Anserwise · VIDraft
• Issues and discussions: please open a thread on the Community tab of this repository.

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Citation

@misc{darwin60b-duo-2026,
  title  = {Darwin-60B-DUO: A single-endpoint DUO of an English-reasoning SOTA
            and a Korean SOTA via Hybrid-A orchestration},
  author = {FINAL-Bench Team and Anserwise Team and VIDRAFT},
  year   = {2026},
  howpublished = {Hugging Face},
  url    = {https://huggingface.co/FINAL-Bench/Darwin-60B-DUO}
}