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
```

Add paper and code links, and library metadata

by nielsr HF Staff - opened 4 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-3

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]:]))
+```