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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-218B-Delphi")
model = AutoModelForMultimodalLM.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
```

SeaWolf-AI commited on 16 days ago

Commit

f2b1050

verified ·

1 Parent(s): b3680b1

Remove Contributors section + simplify citation to team-only

Browse files

Files changed (1) hide show

README.md +1 -11

README.md CHANGED Viewed

@@ -179,22 +179,12 @@ Built upon `CohereLabs/command-a-plus-05-2026-bf16` (Apache-2.0) and `Darwin-218
 ---
-## Contributors
-**Lead Architect & Developer** — 장재원 (Jaewon Jang), CTO, VIDRAFT
-*Domain SFT distillation pipeline, DELPHI cascade design, model integration.*
-**Organization** — VIDRAFT / FINAL-Bench
-https://huggingface.co/FINAL-Bench
----
 ## Citation
 ```bibtex
 @misc{darwin-218b-delphi-2026,
   title  = {Darwin-218B-Delphi: Chemistry-Specialized 218B MoE with DELPHI Cascade Inference},
-  author = {Jang, Jaewon and {VIDRAFT FINAL-Bench Team}},
   year   = {2026},
   publisher = {Hugging Face},
   howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-218B-Delphi}}

 ---
 ## Citation
 ```bibtex
 @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}}