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
```

nielsr HF Staff commited on 14 days ago

Commit

16f6a17

verified ·

1 Parent(s): 7cdfbe6

Add paper and code links, and library metadata

Browse files

Hi! I'm Niels from the Hugging Face community science team. I'm opening this PR to improve the model card for ZAYA1-VL-8B.

Specifically, I have:
- Added `library_name: transformers` to the YAML metadata to improve model discoverability and enable library-specific features.
- Added links to the [ZAYA1-VL-8B Technical Report](https://huggingface.co/papers/2605.08560), the [source code repository](https://github.com/Zyphra/transformers/tree/zaya1-vl), and the official blog post.
- Maintained the existing performance data and architectural information.

These changes help researchers and developers better understand, use, and cite this model.

Files changed (1) hide show

README.md +6 -3

README.md CHANGED Viewed

@@ -1,15 +1,18 @@
 ---
-license: apache-2.0
 base_model:
 - Zyphra/ZAYA1-base
 pipeline_tag: image-text-to-text
 ---
 # ZAYA1-VL-8B
 ZAYA1-VL-8B is a vision-language model (VLM) built upon Zyphra's ZAYA1-8B LLM. It has state-of-the-art performance among VLMs for its size and inference efficiency.
-Learn more about our vision-language models in our [announcement blog post](https://www.zyphra.com/post/zaya1-vl-8b) and our [accompanying technical report](http://www.zyphra.com/zaya1-vl-8b-technical-report)
 ZAYA1-VL-8B is open-sourced under the Apache 2.0 license.
@@ -106,4 +109,4 @@ inputs = {key: value.to(device) for key, value in inputs.items()}
 outputs = model.generate(**inputs, max_new_tokens=100)
 print(processor.tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
-```

 ---
 base_model:
 - Zyphra/ZAYA1-base
+license: apache-2.0
 pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 # ZAYA1-VL-8B
 ZAYA1-VL-8B is a vision-language model (VLM) built upon Zyphra's ZAYA1-8B LLM. It has state-of-the-art performance among VLMs for its size and inference efficiency.
+- **Paper:** [ZAYA1-VL-8B Technical Report](https://huggingface.co/papers/2605.08560)
+- **Code:** [GitHub (zaya1-vl branch)](https://github.com/Zyphra/transformers/tree/zaya1-vl)
+- **Blog:** [Announcement blog post](https://www.zyphra.com/post/zaya1-vl-8b)
 ZAYA1-VL-8B is open-sourced under the Apache 2.0 license.
 outputs = model.generate(**inputs, max_new_tokens=100)
 print(processor.tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
+```