Instructions to use FINAL-Bench/Darwin-218B-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-218B-Expert with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-218B-Expert")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-218B-Expert")
model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/Darwin-218B-Expert", device_map="auto")
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 Settings

vLLM

How to use FINAL-Bench/Darwin-218B-Expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-218B-Expert"
# 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-218B-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-218B-Expert

SGLang

How to use FINAL-Bench/Darwin-218B-Expert 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-218B-Expert" \
    --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-218B-Expert",
		"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-218B-Expert" \
        --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-218B-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-218B-Expert with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-218B-Expert
```

Darwin-218B-Expert / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	- ko
	tags:
	- darwin
	- expert
	- multi-domain
	- task-arithmetic
	- cohere2
	- moe
	base_model: FINAL-Bench/Darwin-218B-kr
	---

	# 🌌 Darwin-218B-Expert

	> VIDRAFT FINAL-Bench
	> Darwin family multi-domain Expert flagship — 218B parameter Cohere2 MoE base with chemistry + biology domain knowledge integrated via task arithmetic on Korean-aligned base.

	A multi-domain "Expert" derivative of the Darwin-218B family. Built by composing chemistry and biology domain deltas onto the Korean-aligned base, this v1 release preserves Korean fluency while adding scientific reasoning in chemistry and biology.

	---

	## Lineage

	```
	CohereLabs/command-a-plus-05-2026-bf16 (base 218B MoE, ~25B active)
	↓ Korean LoRA merge
	Darwin-218B-kr (Korean-aligned base)
	↓ Chem LoRA merge ↓ Bio LoRA merge
	Darwin-218B-Expert-Chem Darwin-218B-Expert-Bio
	↓ task arithmetic ↓
	Darwin-218B-Expert ← THIS MODEL (v1 = Chem + Bio)
	```

	Construction (v1): task arithmetic on the Korean base
	```
	W_Expert = W_Chem + W_Bio − W_kr
	= W_kr + ΔChem + ΔBio
	```

	Both chemistry and biology deltas preserved at 100% (no dilution), with Korean fluency inherited from the kr base.

	---

	## Domain Capabilities

	\| Domain \| Coverage \| Source \|
	\|--------\|----------\|--------\|
	\| Korean (한국어) \| Native fluency \| Inherited from `Darwin-218B-kr` base \|
	\| Chemistry \| Organic, spectroscopy, physical, inorganic, analytical, special (6-domain SFT) \| `Darwin-218B-Expert-Chem` delta (Opus-distilled, anti-contamination) \|
	\| Biology \| Cellular, molecular, biochem, ecology, evolution (6-domain SFT) \| `Darwin-218B-Expert-Bio` delta (Opus-distilled, anti-contamination) \|
	\| General \| All capabilities from Cohere Command A+ retained \| Underlying base \|

	---

	## Architecture

	\| Item \| Value \|
	\|------\|-------\|
	\| Parameters \| 218B total / ~25B active (MoE) \|
	\| Architecture \| `Cohere2VisionForConditionalGeneration` (multimodal-capable, text-primary) \|
	\| Experts \| 128 (Cohere2 MoE structure) \|
	\| Precision \| BF16 \|
	\| Tokenizer \| Cohere2 (vocab 256K) \|
	\| Languages \| English, Korean \|
	\| Context \| 65,536 tokens \|

	---

	## Usage

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

	model = AutoModelForCausalLM.from_pretrained(
	"FINAL-Bench/Darwin-218B-Expert",
	dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)
	tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-218B-Expert")

	messages = [
	{"role": "user", "content": "Explain the mechanism of SN2 reaction step by step. Then describe how mRNA splicing maintains reading frame fidelity."}
	]
	prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tok(prompt, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=2048, temperature=0.3, top_p=0.9)
	print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
	```

	Serving (vLLM, recommended):
	```bash
	vllm serve FINAL-Bench/Darwin-218B-Expert \
	--tensor-parallel-size 8 \
	--dtype bfloat16 \
	--max-model-len 65536 \
	--trust-remote-code
	```

	Requires vLLM ≥ 0.21.0 with `Cohere2VisionForConditionalGeneration` support.

	---

	## Methodology — Task Arithmetic

	Unlike sibling-model averaging (which dilutes expertise), this model uses task vectors (Ilharco et al., 2022) to compose domain knowledge:

	1. Train domain-specific LoRA on a shared base (`Darwin-218B-kr`)
	2. Each merged expert (`Expert-Chem`, `Expert-Bio`) encodes a domain delta from the base
	3. Add deltas to the base: `W_final = W_base + Σ Δ_domains`

	This is mathematically equivalent to applying multiple LoRA adapters simultaneously, but produces a single dense checkpoint requiring no runtime adapter loading.

	Properties:
	- Both expert deltas preserved at 100% magnitude
	- No interference dilution (unlike weighted blends)
	- Korean base layer intact

	---

	## Version Roadmap

	\| Version \| Domains \| Status \|
	\|---------\|---------\|--------\|
	\| v1 \| Korean + Chemistry + Biology \| ✅ Current \|
	\| v2 \| + Mathematics \| ⬜ Planned \|
	\| v3 \| + Code \| ⬜ Planned \|
	\| v4 \| + Physics, Law \| ⬜ Planned \|

	Each domain delta is added via task arithmetic on the shared `Darwin-218B-kr` base.

	---

	## License

	Apache License 2.0

	Built upon `CohereLabs/command-a-plus-05-2026-bf16` (Apache-2.0) and `Darwin-218B-kr` (Apache-2.0). Both upstream components are permissively licensed; users are responsible for compliance with upstream terms.

	---

	## Contributors

	Lead Architect & Developer
	장재원 (Jaewon Jang) — CTO, VIDRAFT
	Multi-domain Expert composition design, task-arithmetic merge pipeline, domain SFT distillation pipelines.

	Organization
	VIDRAFT / FINAL-Bench
	https://huggingface.co/FINAL-Bench

	---

	## Citation

	```bibtex
	@misc{darwin-218b-expert-2026,
	title = {Darwin-218B-Expert: Multi-Domain Expert via Task Arithmetic on Korean-Aligned 218B MoE},
	author = {Jang, Jaewon and {VIDRAFT FINAL-Bench Team}},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-218B-Expert}}
	}
	```

	---

	## References

	- Task Arithmetic: Ilharco et al., "Editing Models with Task Arithmetic", ICLR 2023 — [arXiv:2212.04089](https://arxiv.org/abs/2212.04089)
	- TIES-Merging: Yadav et al., "TIES-Merging: Resolving Interference When Merging Models", NeurIPS 2023 — [arXiv:2306.01708](https://arxiv.org/abs/2306.01708)
	- Cohere Command A+ (base): [CohereLabs/command-a-plus-05-2026-bf16](https://huggingface.co/CohereLabs/command-a-plus-05-2026-bf16)
	- Darwin-218B-kr (Korean base): private — `FINAL-Bench/Darwin-218B-kr`
	- Expert sources: private — `FINAL-Bench/Darwin-218B-Expert-Chem`, `FINAL-Bench/Darwin-218B-Expert-Bio`