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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

SeaWolf-AI commited on 21 days ago

Commit

1aa5cbc

verified ·

1 Parent(s): 492de6f

security: remove leaked Father metadata file

Browse files

Files changed (1) hide show

darwin_mri_report.json +0 -27

darwin_mri_report.json DELETED Viewed

@@ -1,27 +0,0 @@
-{
-  "method": "Darwin V7+ MRI-only (Mother-centric Linear) - dense 27B",
-  "father": "models/Father-Qwen3.6-27B",
-  "mother": "models/Mother-rico03",
-  "mother_bias": 0.85,
-  "category_ratios": {
-    "self_attention": 0.9,
-    "linear_attn": 0.9,
-    "mlp": 0.9,
-    "embedding": 1.0,
-    "lm_head": 1.0,
-    "norm": 0.95,
-    "other": 0.85
-  },
-  "tensor_categories": {
-    "self_attention": 210,
-    "linear_attn": 432,
-    "mlp": 195,
-    "embedding": 5,
-    "lm_head": 1,
-    "norm": 243,
-    "other": 113
-  },
-  "total_tensors": 1199,
-  "total_shards": 14,
-  "elapsed_sec": 252
-}