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

c47eb81

verified ·

1 Parent(s): fc603d7

Add Family Tree (5-member lineage) + datasets metadata

Browse files

Files changed (1) hide show

README.md +44 -7

README.md CHANGED Viewed

@@ -15,9 +15,13 @@ tags:
 - mixture-of-experts
 - cohere2_moe
 - 218b
 base_model:
-- CohereLabs/command-a-plus-05-2026-bf16
 - FINAL-Bench/Darwin-218B-kr
 ---
 # Darwin-218B-Delphi
@@ -55,16 +59,49 @@ DELPHI improvement                           | +4.04pp (+8 questions)
 ---
-## Lineage
 ```
-CohereLabs/command-a-plus-05-2026-bf16     (Apache-2.0 base, 218B MoE, ~25B active, 128 expert)
-              ↓ Korean LoRA merge
-       Darwin-218B-kr                       (Korean-aligned base)
-              ↓ Chemistry SFT LoRA merge   (Opus-distilled, anti-contamination)
-       Darwin-218B-Delphi                   ← THIS MODEL
 ```
 **Distillation**:
 - Teacher: large frontier model (proprietary API; no logits exposure → SFT-on-outputs pattern)
 - 993 high-quality chemistry CoT examples across 6 sub-domains:

 - mixture-of-experts
 - cohere2_moe
 - 218b
+- gpqa-90
 base_model:
 - FINAL-Bench/Darwin-218B-kr
+- CohereLabs/command-a-plus-05-2026-bf16
+base_model_relation: merge
+datasets:
+- FINAL-Bench/darwin-chem-data-v1
 ---
 # Darwin-218B-Delphi
 ---
+## 🌳 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: