Instructions to use FINAL-Bench/Darwin-28B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-28B-Opus with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-28B-Opus")
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("FINAL-Bench/Darwin-28B-Opus")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-28B-Opus")
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 FINAL-Bench/Darwin-28B-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-28B-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-28B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-28B-Opus

SGLang

How to use FINAL-Bench/Darwin-28B-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-28B-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-28B-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-28B-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-28B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-28B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-28B-Opus
```

Darwin-28B-Opus / README.md

SeaWolf-AI

Update README.md

71d2b84 verified about 18 hours ago

preview code

raw

history blame contribute delete

8.97 kB

	---
	license: apache-2.0
	language:
	- en
	- zh
	- ko
	- ja
	- multilingual
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- darwin
	- darwin-v7
	- evolutionary-merge
	- merge
	- mergekit
	- reasoning
	- advanced-reasoning
	- chain-of-thought
	- thinking
	- qwen3.6
	- qwen
	- claude-opus
	- distillation
	- multilingual
	- gpqa
	- benchmark
	- open-source
	- apache-2.0
	- hybrid-vigor
	- proto-agi
	- vidraft
	- eval-results
	base_model:
	- Qwen/Qwen3.6-27B
	- rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled
	base_model_relation: merge
	model-index:
	- name: Darwin-28B-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: 88.89
	name: Accuracy
	verified: false
	---

	# Darwin-28B-Opus — Qwen3.6-27B × Opus-Distilled Evolutionary Merge

	<p align="center">
	<a href="https://huggingface.co/FINAL-Bench/Darwin-28B-Opus"><img src="https://img.shields.io/badge/⭐_GPQA_Diamond-88.89%25_Darwin--28B--Opus-gold?style=for-the-badge" alt="GPQA"></a>
	<a href="https://huggingface.co/FINAL-Bench/Darwin-36B-Opus"><img src="https://img.shields.io/badge/🧬_Sibling-Darwin--36B--Opus_(88.4%25)-blue?style=for-the-badge" alt="36B"></a>
	</p>

	<p align="center">
	<a href="https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis"><img src="https://img.shields.io/badge/🧬_Model-Darwin--4B--Genesis-blue?style=for-the-badge" alt="Genesis"></a>
	<a href="https://huggingface.co/FINAL-Bench/Darwin-9B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--9B--Opus-blue?style=for-the-badge" alt="9B"></a>
	<a href="https://huggingface.co/FINAL-Bench/Darwin-9B-NEG"><img src="https://img.shields.io/badge/⚡_Model-Darwin--9B--NEG_(84.3%25)-purple?style=for-the-badge" alt="NEG"></a>
	<a href="https://huggingface.co/FINAL-Bench/Darwin-27B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--27B--Opus_(86.9%25)-blue?style=for-the-badge" alt="27B"></a>
	</p>

	<p align="center">
	<a href="https://huggingface.co/FINAL-Bench/Darwin-31B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--31B--Opus_(85.9%25)-blue?style=for-the-badge" alt="31B"></a>
	<a href="https://huggingface.co/FINAL-Bench/Darwin-36B-Opus"><img src="https://img.shields.io/badge/⭐_Model-Darwin--36B--Opus_(88.4%25)-blue?style=for-the-badge" alt="36B"></a>
	</p>

	<p align="center">
	<a href="https://huggingface.co/collections/FINAL-Bench/darwin-family"><img src="https://img.shields.io/badge/🏠_Darwin_Family-Collection-green?style=for-the-badge" alt="Family"></a>
	<a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🏆_FINAL_Bench-Leaderboard-green?style=for-the-badge" alt="FINAL Bench"></a>
	</p>

	> Qwen3.6-27B dense · 27.6B parameters · Hybrid Linear/Full Attention · BF16 · Thinking Mode · Apache 2.0
	> Darwin V7 evolutionary merge: Father × Opus-distilled Mother → 88.89% on GPQA Diamond (3-stage adaptive evaluation)

	---

	## Abstract

	Darwin-28B-Opus is the first reasoning model of the Darwin series built on the Qwen3.6 generation backbone. Produced by the Darwin V7 evolutionary breeding engine from two publicly available parents, it combines the strong bilingual reasoning of Qwen3.6-27B with Claude Opus 4-style chain-of-thought distilled behaviour.

	On the GPQA Diamond graduate-level reasoning benchmark (198 PhD-level questions), Darwin-28B-Opus scores 88.89 % under the standard 3-stage adaptive evaluation, slightly edging out its larger MoE sibling Darwin-36B-Opus (88.4 %) and clearly surpassing its Qwen3.5-generation counterpart Darwin-27B-Opus (86.9 %).

	---

	## 🧬 Model Lineage

	\| Role \| Model \| Role in the Merge \|
	\|:---:\|:---\|:---\|
	\| Father (父) \| [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) \| Qwen3.6 generation dense backbone with hybrid linear/full attention. \|
	\| Mother (母) \| [`rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled`](https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled) \| Claude Opus reasoning-distilled variant of the same backbone (Jackrong-style distillation, 14 k traces). \|
	\| Offspring \| `Darwin-28B-Opus` (this model) \| Darwin V7 evolutionary merge; Qwen3.6 architecture retained, Opus reasoning style inherited. \|

	> Why 28B? The `28B` label denotes the Qwen3.6-generation member of the Darwin lineup (`+1` over the Qwen3.5-era `Darwin-27B-Opus`).
	> The actual parameter count is 27.6 B, and the architecture exactly follows Qwen3.6-27B.

	---

	## ⚙️ Technical Specifications

	\| Component \| Value \|
	\|:---\|:---\|
	\| Architecture \| `Qwen3_5ForConditionalGeneration` (Qwen3.6 generation, hybrid linear + full attention) \|
	\| Parameters \| 27.6 B (BF16) \|
	\| Hidden size \| 5 120 \|
	\| Intermediate size \| 17 408 \|
	\| Head dim \| 256 \|
	\| Layers \| 64 (3 linear : 1 full attention, `full_attention_interval = 4`) \|
	\| Precision \| bfloat16 \|
	\| Context length \| Inherited from base (long-chain reasoning supported) \|
	\| License \| Apache 2.0 \|

	---

	## 🏆 Benchmark — GPQA Diamond (198 questions)

	Darwin-28B-Opus is evaluated under our standard 3-stage adaptive evaluation protocol, identical to the protocol used across the Darwin series.

	\| Stage \| Decoding Protocol \| Cost \| Accuracy \|
	\|:---:\|:---\|:---:\|:---:\|
	\| Stage 1 \| Single-shot greedy baseline \| 1× \| 74.75 % (148 / 198) \|
	\| Stage 2 \| Majority vote ×8 at temperature 0.7 on Stage-1 wrongs \| 8× \| 83.84 % (166 / 198) \|
	\| Stage 3 \| Adaptive ensemble refinement (close-tie tiebreaker + iterative MTI on residual hard questions) \| ≈ 20× \| 🥇 88.89 % (176 / 198) \|

	Key performance indicators:
	- Stage 1 → Stage 3: +14.14 %p through adaptive protocol
	- vs Darwin-27B-Opus (86.9 %): +1.99 %p
	- vs Darwin-36B-Opus (88.4 %): +0.49 %p
	- vs Darwin-31B-Opus (85.9 %): +2.99 %p

	---

	## 🚀 Usage

	### Standard inference (Stage 1 baseline)

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	tok = AutoTokenizer.from_pretrained(
	"FINAL-Bench/Darwin-28B-Opus",
	trust_remote_code=True,
	)
	model = AutoModelForCausalLM.from_pretrained(
	"FINAL-Bench/Darwin-28B-Opus",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)

	messages = [
	{"role": "user",
	"content": "Solve: If f(x) = x³ − 3x + 2, find all critical points and classify them."}
	]
	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=2048, do_sample=False)
	print(tok.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
	```

	### Enhanced accuracy (Stage 2-3 adaptive)

	For leaderboard-grade accuracy, combine:
	1. Stage 1 greedy baseline,
	2. Stage 2 maj@8 temperature sampling on low-confidence answers,
	3. Stage 3 adaptive refinement on still-disputed answers.

	Reference implementation is provided in the Darwin-series evaluation harness.

	---

	## 🎯 Recommended Use-Cases

	- Graduate-level STEM reasoning (GPQA / science qualifying exams)
	- Mathematical problem solving (MATH, AIME-style problems)
	- Code generation and debugging (HumanEval, MBPP)
	- Complex multi-step chain-of-thought tasks
	- Bilingual reasoning (strong English + Korean; also Chinese / Japanese)

	## ⚠️ Limitations

	- At 27.6 B parameters in bfloat16, full inference requires ≈ 55 GB of VRAM (e.g., a single A100-80GB or B200).
	- Optimised for English first, with secondary support for Korean, Chinese, and Japanese.
	- Deep Opus-style reasoning traces tend to be verbose — control with `max_new_tokens` as needed.

	---

	## 📚 Citation

	```bibtex
	@misc{darwin28b_opus_2026,
	title = {Darwin-28B-Opus: Evolutionary Merging of Qwen3.6-27B with Claude-Opus-Distilled Reasoning},
	author = {FINAL-Bench / Darwin Research Team},
	year = {2026},
	howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-28B-Opus}},
	note = {Darwin V7 · Mother-centric Ratio Interpolation merge · 88.89 % GPQA Diamond (3-stage)}
	}
	```

	---

	## 🔗 Related Darwin Models

	- Darwin-36B-Opus — MoE 36B, Qwen3.6-35B-A3B × Opus distilled, GPQA 88.4 %
	- Darwin-31B-Opus — 31B dense, multilingual-strong reasoning, GPQA 85.9 %
	- Darwin-27B-Opus — 27B dense (Qwen3.5 generation), GPQA 86.9 %
	- Darwin-9B-NEG — 9B with Native Entropy Gating, GPQA 84.3 %
	- Darwin-9B-Opus — the Qwen3.5-9B Darwin member
	- Darwin-4B-Genesis — smallest Darwin member

	---

	This model is introduced in [Darwin Family](https://arxiv.org/abs/2605.14386).

	Darwin V7 · Qwen3.6 generation flagship · Sealed 2026-04-25 · FINAL-Bench