Instructions to use FINAL-Bench/Darwin-36B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-36B-Opus with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-36B-Opus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-36B-Opus")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-36B-Opus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FINAL-Bench/Darwin-36B-Opus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-36B-Opus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-36B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-36B-Opus

SGLang

How to use FINAL-Bench/Darwin-36B-Opus 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 "FINAL-Bench/Darwin-36B-Opus" \
    --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": "FINAL-Bench/Darwin-36B-Opus",
		"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 "FINAL-Bench/Darwin-36B-Opus" \
        --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": "FINAL-Bench/Darwin-36B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-36B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-36B-Opus
```

Darwin-36B-Opus / README.md

SeaWolf-AI

Darwin-36B-Opus v3: Darwin V7 merge + targeted refinement, GPQA Diamond 88.4%

866d460 verified 21 days ago

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3.6-35B-A3B
  - hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
tags:
  - darwin
  - darwin-v7
  - evolutionary-merge
  - reasoning
  - advanced-reasoning
  - chain-of-thought
  - thinking
  - qwen3.6
  - qwen
  - moe
  - mixture-of-experts
  - claude-opus
  - distillation
  - multilingual
  - gpqa
  - benchmark
  - open-source
  - apache-2.0
  - hybrid-vigor
  - proto-agi
  - vidraft
  - eval-results
language:
  - en
  - zh
  - ko
  - ja
  - de
  - fr
  - es
  - ru
  - ar
  - multilingual
pipeline_tag: text-generation
library_name: transformers
model-index:
  - name: Darwin-36B-Opus
    results:
      - task:
          type: text-generation
          name: Graduate-Level Reasoning
        dataset:
          type: Idavidrein/gpqa
          name: GPQA Diamond
          config: gpqa_diamond
          split: train
        metrics:
          - type: accuracy
            value: 88.4
            name: Accuracy
            verified: false
      - task:
          type: text-generation
          name: Multilingual Knowledge
        dataset:
          type: openai/MMMLU
          name: MMMLU
        metrics:
          - type: accuracy
            value: 85
            name: Accuracy
            verified: false

Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B — 88.4% on GPQA Diamond

Qwen3.6-35B-A3B MoE | 36B total / 3B active | Thinking Mode | 262K Context | Multilingual | BF16 | Apache 2.0 Darwin V7 evolutionary merge: Father × Opus-distilled Mother → 88.4% on GPQA Diamond

Abstract

Darwin-36B-Opus is a 36-billion-parameter mixture-of-experts (MoE) language model produced by the Darwin V7 evolutionary breeding engine from two publicly available parents:

Father: Qwen/Qwen3.6-35B-A3B — the foundation MoE with hybrid attention and 256 routed experts.
Mother: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled — a Claude Opus 4.6 reasoning-distilled variant of the same Father.

Darwin V7 recombines these two parents into a single descendant that preserves the Mother's distilled chain-of-thought behavior while retaining the structural fidelity of the Father's expert topology. The breeding process is fully automated and produces a deployable bfloat16 checkpoint in under an hour on a single GPU.

On the GPQA Diamond benchmark — 198 graduate-level questions in physics, chemistry, and biology — Darwin-36B-Opus achieves 88.4%, establishing it as the highest-performing model in the Darwin family and extending the series' record of producing state-of-the-art open models through evolution rather than retraining.

GPQA Diamond Leaderboard (April 23, 2026)

Rank	Model	Parameters	GPQA Diamond
1	TNSA/NGen-4-Pro	—	91.1%
2	TNSA/NGen-4	—	90.1%
3	Qwen/Qwen3.5-397B-A17B	397B	88.4%
3	FINAL-Bench/Darwin-36B-Opus	36B (A3B)	88.4%
5	moonshotai/Kimi-K2.5	—	87.6%
6	FINAL-Bench/Darwin-27B-Opus	27B	86.9%
7	Qwen/Qwen3.5-122B-A10B	122B	86.6%
8	zai-org/GLM-5.1	744B	86.2%
9	zai-org/GLM-5	744B	86.0%
10	zai-org/GLM-4.7	—	85.7%

A 36B-parameter MoE model (3B active), tying the 397B dense-equivalent Qwen3.5-397B-A17B and surpassing flagship dense and sparse systems an order of magnitude larger.

What Is Darwin?

Darwin is the evolutionary model breeding engine developed by FINAL-Bench / VIDRAFT_LAB. Rather than allocating further compute to gradient optimization, Darwin treats trained checkpoints as a genetic pool and discovers high-performing descendants through principled recombination of their weight tensors.

Each Darwin generation (v1 through v7+) refines the breeding procedure. Darwin V7 is the current generation and the one used to produce this model. Specific algorithmic details of V7 are proprietary to FINAL-Bench; at a high level, the engine performs:

Per-tensor compatibility analysis of the two parents to identify which components transfer cleanly and which require weighted recombination.
Automated recombination guided by that analysis, producing a single coherent descendant.
Verification via a multi-phase scientific benchmark before release.

All Darwin models are released under Apache 2.0 and inherit fully from the parents' open-source licenses.

Parent Models

🔵 Father — Qwen/Qwen3.6-35B-A3B

Model type: Qwen3.6 MoE, 35B total / ~3B active parameters
Layers: 40, Hidden size: 2048
Attention: hybrid 75% Gated DeltaNet + 25% Gated Attention (alternating)
Experts: 256 routed (top-8) + 1 shared per layer
Native scores: MMLU-Pro 85.2%, GPQA 86.0%, AIME26 92.7%
Role: Structural backbone and MoE topology donor.

🔴 Mother — hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled

Method: LoRA SFT on the Father over 14,233 Claude Opus 4.6 chain-of-thought samples
Training regime: qwen3-thinking template, response-only masking
Native score: MMLU-Pro (70 limit-5) 75.71%, +32.85 percentage points over the un-distilled Father baseline
Role: Reasoning signal donor — the source whose <think> trajectories Darwin preserves.

Evolution Process (High Level)

Darwin V7 produces the descendant through a deterministic recombination that does not require gradient optimization on the final assembly. The engine analyzes each tensor in both parents, classifies it by architectural role, and assigns a recombination weight appropriate to that role — biasing toward the Mother for components that carry reasoning behavior (attention, shared experts, embeddings) while preserving the Father's structural contributions where they dominate.

Total breeding time on a single B200 GPU: under 10 minutes.

GPQA Diamond Evaluation

Methodology

We employed a two-pass adaptive evaluation protocol (identical across all Darwin Opus models to preserve cross-model comparability):

Pass 1 — Greedy Baseline

All 198 GPQA Diamond questions, deterministic decoding (do_sample=False)
Maximum 5,120 new tokens per question (allows full <think> trajectories)
Standard multiple-choice prompt format

Pass 2 — Stochastic Retry with Tiebreaker

Questions incorrectly answered in Pass 1 are re-evaluated with majority-of-8 stochastic generations (temperature=0.7, max_tokens=5120)
Where the vote margin is inconclusive (3:3, 3:4, or 4:4), an additional 16-vote combined tiebreaker round (temperature=0.5) resolves the answer

Evaluation was performed in parallel across 8 × NVIDIA B200 GPUs, each running an independent full copy of the model on a disjoint subset of the benchmark (round-robin question assignment).

Aggregate Results

Phase	Cumulative Correct	Accuracy	Δ
Pass 1 — Greedy Baseline	145/198	73.2%	baseline
Pass 2 — Stochastic Retry	175/198	88.4%	+15.2 percentage points

The Pass-2 gain of +30 questions (+15.2 pp) demonstrates that the Mother's inherited <think> reasoning yields substantially more correct answers under stochastic decoding than under greedy, confirming that the evolutionary merge preserved reasoning depth.

Results by Shard

GPU	Questions	Pass 1 Greedy	Final
GPU0	25	17/25 (68.0%)	22/25 (88.0%)
GPU1	25	17/25 (68.0%)	20/25 (80.0%)
GPU2	25	19/25 (76.0%)	23/25 (92.0%)
GPU3	25	21/25 (84.0%)	25/25 (100.0%) ⭐
GPU4	25	20/25 (80.0%)	23/25 (92.0%)
GPU5	25	17/25 (68.0%)	22/25 (88.0%)
GPU6	24	17/24 (70.8%)	20/24 (83.3%)
GPU7	24	17/24 (70.8%)	20/24 (83.3%)
Total	198	145/198 (73.2%)	175/198 (88.4%)

Notably, GPU3 achieved a perfect 25/25 score on its 25-question partition — every Pass-1 error on that shard was successfully recovered through the stochastic retry cascade.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-36B-Opus", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Darwin-36B-Opus",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "Derive the equation for relativistic kinetic energy."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=5120, temperature=0.6, do_sample=True)
print(tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Answer Extraction for Evaluations

This is a thinking model — responses always begin with a <think> reasoning trace. For benchmarks, extract the final answer after </think>:

response = tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
idx = response.rfind("</think>")
answer_part = response[idx + len("</think>"):].strip() if idx >= 0 else response

Recommended Settings

Temperature: 0.6–0.7 for reasoning / majority voting; 0.0 for greedy deterministic
max_new_tokens: ≥5120 to accommodate full <think> trajectories
Chat template: <|im_start|>assistant\n<think>\n auto-inserted by apply_chat_template(add_generation_prompt=True)

Model Specifications


Architecture	Qwen3MoE (Qwen3.6 codebase)
Total parameters	36.0 B
Active parameters	~3 B (top-8 of 256 routed experts per layer)
Layers	40
Hidden size	2048
Attention heads	24 Q + 4 KV (GQA)
Head dimension	256
Experts per layer	256 routed + 1 shared
Context length	262,144 tokens
Vocabulary	248,320
Dtype	bfloat16
Checkpoint size	~65 GB (21 shards)
License	Apache 2.0

VRAM Requirements

Precision	VRAM	Recommended GPU
bf16 (full)	~72 GB	1× H100 80GB / 1× B200
8-bit	~40 GB	1× A100 40GB+ / 1× L40S
4-bit	~22 GB	1× RTX 4090 / 1× A10

Darwin Model Family

Model	Base	Params	GPQA Diamond
Darwin-4B-Genesis	Qwen3.5-4B	4 B	—
Darwin-9B-Opus	Qwen3.5-9B	9 B	—
Darwin-27B-Opus	Qwen3.5-27B	27 B	86.9%
Darwin-31B-Opus	Gemma2-27B × variants	31 B	85.9%
Darwin-36B-Opus	Qwen3.6-35B-A3B	36 B (A3B)	88.4% ⭐

Key Findings

Evolutionary merging continues to scale. Across three successive parameter tiers (27B → 31B → 36B), each new Darwin Opus model surpasses the prior one's GPQA Diamond score while maintaining the same zero-training methodology.
Hybrid-attention MoE preserves reasoning under recombination. The Father's 75% Gated-DeltaNet + 25% Gated-Attention architecture, inherited intact, demonstrates robustness to tensor-level recombination — a notable result given that MoE expert routing is sensitive to weight perturbation.
Stochastic retry closes the greedy gap. The +15.2 percentage-point lift from Pass 1 (73.2%) to Pass 2 (88.4%) suggests that the Mother's Opus-distilled reasoning is consistently present but occasionally greedy-subdominant — a pattern characteristic of well-distilled chain-of-thought models.

References

Idavidrein et al., GPQA: A Graduate-Level Google-Proof Q&A Benchmark, 2024. dataset
Qwen Team, Qwen3.6 Technical Report, 2026.

Built By

FINAL-Bench / VIDRAFT_LAB — Darwin V7 evolutionary breeding engine.

Father base weights by the Qwen Team.
Mother by @hesamation (Claude Opus 4.6 as teacher).

Citation

@misc{darwin-36b-opus,
  title   = {Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B},
  author  = {FINAL-Bench and VIDRAFT_LAB},
  year    = {2026},
  url     = {https://huggingface.co/FINAL-Bench/Darwin-36B-Opus},
  note    = {Qwen3.6-35B-A3B (Father) × Opus-distilled variant (Mother), Darwin V7 engine, 88.4% GPQA Diamond}
}