Instructions to use Zyphra/ZAYA1-VL-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zyphra/ZAYA1-VL-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Zyphra/ZAYA1-VL-8B")
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 AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Zyphra/ZAYA1-VL-8B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zyphra/ZAYA1-VL-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zyphra/ZAYA1-VL-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zyphra/ZAYA1-VL-8B",
		"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/Zyphra/ZAYA1-VL-8B

SGLang

How to use Zyphra/ZAYA1-VL-8B 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 "Zyphra/ZAYA1-VL-8B" \
    --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": "Zyphra/ZAYA1-VL-8B",
		"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 "Zyphra/ZAYA1-VL-8B" \
        --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": "Zyphra/ZAYA1-VL-8B",
		"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 Zyphra/ZAYA1-VL-8B with Docker Model Runner:
```
docker model run hf.co/Zyphra/ZAYA1-VL-8B
```

hassansh commited on 2 days ago

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@@ -40,17 +40,17 @@ ZAYA1-VL-8B is trained only upon open data. Detailed dataset descriptions can be
 |---|---:|---:|---:|---:|---:|---:|
 | AI2D (test) | **87.5** | <u>73.6</u> | 78.6 | 85.5 | 85.4 | 83.7 |
 | ChartQA (test) | 82.2 | <u>77.9</u> | 78.4 | **87.0** | 86.1 | 82.4 |
-| DocVQA (test) | 92.5 | <u>77.7</u> | -- | 92.9 | 87.8 | -- |
-| InfoVQA (test) | 74.0 | <u>53.9</u> | -- | 78.1 | 78.6 | -- |
 | TextVQA (val) | <u>74.4</u> | 78.1 | 79.0 | 78.5 | **83.1** | 81.1 |
 | OCRBench | 79.8 | <u>55.0</u> | 83.1 | **86.7** | 62.0 | 85.3 |
-| VQA v2.0 (val) | 80.0 | 82.8 | 78.3 | 78.4 | **85.3** | 80.4 |
 | MathVista (mini) | 64.0 | <u>39.1</u> | 52.9 | 73.5 | 56.5 | **82.3** |
-| MMMU (val) | 46.0 | -- | 49.2 | **72.6** | <u>48.8</u> | 56.9 |
 | SEED (image) | 72.7 | <u>68.7</u> | 75.8 | 76.8 | **78.0** | 76.6 |
 | Blink (val) | <u>45.9</u> | -- | 61.0 | 58.9 | **63.5** | 56.8 |
 | RealWorldQA | 65.0 | <u>60.4</u> | 69.0 | 71.2 | 73.8 | **74.2** |
-| CountBenchQA | 88.1 | 77.4 | 84.2 | 82.1 | **91.2** | 84.8 |
 | PixMoCount (test) | 83.1 | <u>45.2</u> | 65.5 | 47.3 | **87.0** | 84.2 |
 | Point-Bench (avg) | 58.0 | 58.0 | <u>40.6</u> | -- | **68.5** | 64.4 |
 | RefCOCO (avg) | 84.3 | -- | <u>80.1</u> | **89.1** | -- | 87.7 |

 |---|---:|---:|---:|---:|---:|---:|
 | AI2D (test) | **87.5** | <u>73.6</u> | 78.6 | 85.5 | 85.4 | 83.7 |
 | ChartQA (test) | 82.2 | <u>77.9</u> | 78.4 | **87.0** | 86.1 | 82.4 |
+| DocVQA (test) | 92.5 | <u>77.7</u> | -- | **92.9** | 87.8 | -- |
+| InfoVQA (test) | 74.0 | <u>53.9</u> | -- | 78.1 | **78.6** | -- |
 | TextVQA (val) | <u>74.4</u> | 78.1 | 79.0 | 78.5 | **83.1** | 81.1 |
 | OCRBench | 79.8 | <u>55.0</u> | 83.1 | **86.7** | 62.0 | 85.3 |
+| VQA v2.0 (val) | 80.0 | 82.8 | <u>78.3</u> | 78.4 | **85.3** | 80.4 |
 | MathVista (mini) | 64.0 | <u>39.1</u> | 52.9 | 73.5 | 56.5 | **82.3** |
+| MMMU (val) | <u>46.0</u> | -- | 49.2 | **72.6** | 48.8 | 56.9 |
 | SEED (image) | 72.7 | <u>68.7</u> | 75.8 | 76.8 | **78.0** | 76.6 |
 | Blink (val) | <u>45.9</u> | -- | 61.0 | 58.9 | **63.5** | 56.8 |
 | RealWorldQA | 65.0 | <u>60.4</u> | 69.0 | 71.2 | 73.8 | **74.2** |
+| CountBenchQA | 88.1 | <u>77.4</u> | 84.2 | 82.1 | **91.2** | 84.8 |
 | PixMoCount (test) | 83.1 | <u>45.2</u> | 65.5 | 47.3 | **87.0** | 84.2 |
 | Point-Bench (avg) | 58.0 | 58.0 | <u>40.6</u> | -- | **68.5** | 64.4 |
 | RefCOCO (avg) | 84.3 | -- | <u>80.1</u> | **89.1** | -- | 87.7 |