Instructions to use endless-frontier/BigBang-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use endless-frontier/BigBang-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="endless-frontier/BigBang-v1")
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("endless-frontier/BigBang-v1")
model = AutoModelForMultimodalLM.from_pretrained("endless-frontier/BigBang-v1", device_map="auto")
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 endless-frontier/BigBang-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "endless-frontier/BigBang-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "endless-frontier/BigBang-v1",
		"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/endless-frontier/BigBang-v1

SGLang

How to use endless-frontier/BigBang-v1 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 "endless-frontier/BigBang-v1" \
    --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": "endless-frontier/BigBang-v1",
		"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 "endless-frontier/BigBang-v1" \
        --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": "endless-frontier/BigBang-v1",
		"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 endless-frontier/BigBang-v1 with Docker Model Runner:
```
docker model run hf.co/endless-frontier/BigBang-v1
```

Update xbench results of the ds-v4-flash preview

by gokoururi123 - opened about 9 hours ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -34,7 +34,7 @@ Comparison with representative closed- and open-source frontier models, together
 |:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
 | **Long-horizon Search** |  |  |  |  |  |  |  |  |  |  |  |  |
 | BrowseComp | 84.3 | 85.9 | 84.4 | 68.7 | 73.2 | 83.4 | 75.8 | 67.9 | 74.1 | 48.5 | 73.9 | **76.5** |
-| XBench | 61.4 | - | 72.4 | 65.8 | 66.0 | 64.8 | 50.8 | 32.6 | 57.2 | 52.4 | 61.8 | **58.4** |
 | **Coding Tasks** |  |  |  |  |  |  |  |  |  |  |  |  |
 | SWE-Bench Pro | 69.2 | 54.2 | 58.6 | 62.1 | 52.6 | 55.4 | 56.3 | 43.6 | 50.2 | 42.3 | 38.7 | **54.2** |
 | SciCode-V-Sub | 92.3 | - | 95.1 | 84.3 | 83.7 | 90.2 | - | 56.5 | 39.0 | 64.1 | - | **68.6** |

 |:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
 | **Long-horizon Search** |  |  |  |  |  |  |  |  |  |  |  |  |
 | BrowseComp | 84.3 | 85.9 | 84.4 | 68.7 | 73.2 | 83.4 | 75.8 | 67.9 | 74.1 | 48.5 | 73.9 | **76.5** |
+| XBench | 61.4 | - | 72.4 | 65.8 | 62.2 | 64.8 | 50.8 | 32.6 | 57.2 | 52.4 | 61.8 | **58.4** |
 | **Coding Tasks** |  |  |  |  |  |  |  |  |  |  |  |  |
 | SWE-Bench Pro | 69.2 | 54.2 | 58.6 | 62.1 | 52.6 | 55.4 | 56.3 | 43.6 | 50.2 | 42.3 | 38.7 | **54.2** |
 | SciCode-V-Sub | 92.3 | - | 95.1 | 84.3 | 83.7 | 90.2 | - | 56.5 | 39.0 | 64.1 | - | **68.6** |