Instructions to use FINAL-Bench/Darwin-35B-A3B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-35B-A3B-Opus with Transformers:

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

pipe = pipeline("image-text-to-text", model="FINAL-Bench/Darwin-35B-A3B-Opus")
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-35B-A3B-Opus")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-35B-A3B-Opus")
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-35B-A3B-Opus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-35B-A3B-Opus"
# 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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus

SGLang

How to use FINAL-Bench/Darwin-35B-A3B-Opus 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-35B-A3B-Opus" \
    --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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-35B-A3B-Opus" \
        --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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-35B-A3B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus
```

Update pipeline_tag in README.md

by TahirC - opened Apr 3

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+68

-68

Files changed (1) hide show

README.md +68 -68

README.md CHANGED Viewed

@@ -1,78 +1,78 @@
 ---
 license: apache-2.0
 base_model:
-  - Qwen/Qwen3.5-35B-A3B
-  - Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
 tags:
-  - merge
-  - evolutionary-merge
-  - darwin
-  - darwin-v5
-  - model-mri
-  - reasoning
-  - advanced-reasoning
-  - chain-of-thought
-  - thinking
-  - qwen3.5
-  - qwen
-  - moe
-  - mixture-of-experts
-  - claude-opus
-  - distillation
-  - multimodal
-  - vision-language
-  - multilingual
-  - gpqa
-  - benchmark
-  - open-source
-  - apache-2.0
-  - layer-wise-merge
-  - moe-merge
-  - dead-expert-revival
-  - coding-agent
-  - tool-calling
-  - long-context
-  - 262k-context
 language:
-  - en
-  - zh
-  - ko
-  - ja
-  - de
-  - fr
-  - es
-  - ru
-  - ar
-  - multilingual
-pipeline_tag: text-generation
 library_name: transformers
 model-index:
-  - name: Darwin-35B-A3B-Opus
-    results:
-      - task:
-          type: text-generation
-          name: Graduate-Level Reasoning
-        dataset:
-          type: Idavidrein/gpqa
-          name: GPQA Diamond
-          config: gpqa_diamond
-          split: train
-        metrics:
-          - type: accuracy
-            value: 90.0
-            name: Accuracy
-            verified: false
-      - task:
-          type: text-generation
-          name: Multilingual Knowledge
-        dataset:
-          type: openai/MMMLU
-          name: MMMLU
-        metrics:
-          - type: accuracy
-            value: 85.0
-            name: Accuracy
-            verified: false
 ---
 # Darwin-35B-A3B-Opus

 ---
 license: apache-2.0
 base_model:
+- Qwen/Qwen3.5-35B-A3B
+- Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
 tags:
+- merge
+- evolutionary-merge
+- darwin
+- darwin-v5
+- model-mri
+- reasoning
+- advanced-reasoning
+- chain-of-thought
+- thinking
+- qwen3.5
+- qwen
+- moe
+- mixture-of-experts
+- claude-opus
+- distillation
+- multimodal
+- vision-language
+- multilingual
+- gpqa
+- benchmark
+- open-source
+- apache-2.0
+- layer-wise-merge
+- moe-merge
+- dead-expert-revival
+- coding-agent
+- tool-calling
+- long-context
+- 262k-context
 language:
+- en
+- zh
+- ko
+- ja
+- de
+- fr
+- es
+- ru
+- ar
+- multilingual
+pipeline_tag: image-text-to-text
 library_name: transformers
 model-index:
+- name: Darwin-35B-A3B-Opus
+  results:
+  - task:
+      type: text-generation
+      name: Graduate-Level Reasoning
+    dataset:
+      type: Idavidrein/gpqa
+      name: GPQA Diamond
+      config: gpqa_diamond
+      split: train
+    metrics:
+    - type: accuracy
+      value: 90
+      name: Accuracy
+      verified: false
+  - task:
+      type: text-generation
+      name: Multilingual Knowledge
+    dataset:
+      type: openai/MMMLU
+      name: MMMLU
+    metrics:
+    - type: accuracy
+      value: 85
+      name: Accuracy
+      verified: false
 ---
 # Darwin-35B-A3B-Opus