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

Libraries

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

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-218B-Delphi")
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-218B-Delphi")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-218B-Delphi")
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-Delphi 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-Delphi"
# 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-Delphi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Darwin-218B-Delphi

VIDRAFT FINAL-Bench — chemistry-specialized 218B MoE, served via the DELPHI 5-Phase inference cascade.

A chemistry-domain derivative of the Darwin-218B family. Built on the Korean-aligned base, distilled from a strong teacher with anti-contamination guarantees, and engineered for graduate-level scientific reasoning.

🏆 GPQA Diamond — Public Results

GPQA Diamond (198 questions) — Darwin-218B-Delphi
─────────────────────────────────────────────────────────────
Method                                       | Accuracy
─────────────────────────────────────────────────────────────
Darwin-218B-Delphi baseline (MAJ@8)          | 86.87%  (172/198)
Darwin-218B-Delphi (DELPHI cascade)          | 90.91%  (180/198)
─────────────────────────────────────────────────────────────
DELPHI improvement                           | +4.04pp (+8 questions)

Reference baselines (vendor-reported)

Model	GPQA Diamond	Mode
GPT-5 (OpenAI)	88.0%	thinking
Claude Opus 4.5 (Anthropic)	91.8%	extended thinking
DeepSeek-V3.2	~78-82%	standard
Darwin-218B-Delphi (MAJ@8)	86.87%	standard
Darwin-218B-Delphi (DELPHI)	90.91%	VIDRAFT signature

→ DELPHI cascade로 Claude Opus 4.5 extended thinking 동급권 진입.

🌳 Family Tree (족보)

        🧓 GRANDFATHER (조부)                    🧓 GRANDMOTHER (조모)
        ───────────────────                      ───────────────────
        CohereLabs/                              Anthropic Claude
        command-a-plus-05-2026-bf16              Opus 4.5
        (Apache-2.0)                             (chemistry knowledge donor)
        218B MoE / ~25B active                   via SFT distillation
        128 experts, BF16                        (no logits, output-only)
                  │                                       │
                  │                                       │
                  └────────────────┬──────────────────────┘
                                   │
                                   ▼
        👨 FATHER (부친)                         👩 MOTHER (모친)
        ───────────────────                      ───────────────────
        FINAL-Bench/                             FINAL-Bench/
        Darwin-218B-kr                           darwin-chem-data-v1
        (Korean LoRA merged)                     (993 chemistry CoT samples,
        Korean fluency layer                      6 sub-domains,
                                                  anti-contamination guaranteed)
                  │                                       │
                  │                                       │
                  └────────────────┬──────────────────────┘
                                   │
                                   ▼
                        👦 CHILD (자식 / THIS MODEL)
                        ──────────────────────────────
                        FINAL-Bench/Darwin-218B-Delphi
                        ──────────────────────────────
                        • Korean + Chemistry specialist
                        • 218B MoE, ~25B active
                        • Apache-2.0
                        • GPQA Diamond 90.91% (DELPHI cascade)
                        • Served via DELPHI 5-Phase inference

Lineage notes

Paternal line (모델 골격): Cohere Command A+ → Korean LoRA → Chemistry LoRA merge → Delphi
Maternal line (지식 source): Claude Opus 4.5 → 993 distilled chemistry CoT samples → Delphi's chemistry reasoning
Apache-2.0 compatibility: All ancestors (paternal line) are Apache-2.0 licensed; maternal line is data-only output (Anthropic ToS compliant for derivative model training)

Distillation:

Teacher: large frontier model (proprietary API; no logits exposure → SFT-on-outputs pattern)
993 high-quality chemistry CoT examples across 6 sub-domains: organic, spectroscopy, physical, inorganic, analytical, special
Anti-contamination: GPQA Diamond 198 questions guaranteed not in training data
LoRA: r=16, α=32, q/k/v/o, lr=1e-5, 1 epoch, max_length=3072
Trained on Darwin-218B-kr (S4 6×B200 bf16)
Merge: full dense checkpoint, no runtime adapter loading

Architecture

Item	Value
Total parameters	218B
Active parameters	~25B (MoE)
Experts	128 (Cohere2 MoE)
Precision	BF16
Architecture	`Cohere2VisionForConditionalGeneration` (multimodal-capable, text-primary)
Tokenizer	Cohere2 (vocab 256K)
Languages	English, Korean
Context	65,536 tokens
License	Apache-2.0

Usage

vLLM (recommended)

vllm serve FINAL-Bench/Darwin-218B-Delphi \
    --tensor-parallel-size 8 \
    --dtype bfloat16 \
    --max-model-len 65536 \
    --trust-remote-code \
    --enforce-eager \
    --limit-mm-per-prompt '{"image":0,"video":0}'

Requires vLLM ≥ 0.21.0 (Cohere2VisionForConditionalGeneration support).

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

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

messages = [
    {"role": "user", "content": "Explain the SN2 mechanism step by step, "
                                "then justify why CH3I reacts faster than CH3Cl."}
]
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))

License

Apache License 2.0

Built upon CohereLabs/command-a-plus-05-2026-bf16 (Apache-2.0) and Darwin-218B-kr (Apache-2.0). All upstream components are permissively licensed.

Citation

@misc{darwin-218b-delphi-2026,
  title  = {Darwin-218B-Delphi: Chemistry-Specialized 218B MoE with DELPHI Cascade Inference},
  author = {{VIDRAFT FINAL-Bench Team}},
  year   = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-218B-Delphi}}
}