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 17 days ago

Commit

e9b74a6

verified ·

1 Parent(s): 845e2f2

Update README.md

Browse files

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README.md +20 -6

README.md CHANGED Viewed

@@ -66,14 +66,28 @@ pip install "transformers[dev-torch] @ git+https://github.com/Zyphra/transformer
 TODO
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-tokenizer = AutoTokenizer.from_pretrained("Zyphra/ZAYA1-base")
-model = AutoModelForCausalLM.from_pretrained("Zyphra/ZAYA1-base", device_map="cuda", dtype=torch.bfloat16)
-input_text = "What factors contributed to the fall of the Roman Empire?"
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 outputs = model.generate(**input_ids, max_new_tokens=100)
 print(tokenizer.decode(outputs[0]))

 TODO
 ```python
+from transformers import Zaya1VLForConditionalGeneration, Zaya1VLProcessor
 import torch
+device = "cuda"
+processor = Zaya1VLProcessor.from_pretrained("Zyphra/ZAYA1-VL", temporal_patch_size=1)
+model = Zaya1VLForConditionalGeneration.from_pretrained("Zyphra/ZAYA1-VL", device_map=device, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
+question = "What do you see in the image? Give us some detail."
+add_special_tokens = True
+num_img_tokens = 8000
+conversation = [
+    {"role": "user", "content": [
+        {"type": "image", "image": image, "max_pixels" : num_img_tokens * 28 * 28, "min_pixels" : 10 * 28 * 28},
+        {"type": "text", "text": question},
+      ]
+    },
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+images, _ = process_vision_info(conversation)
+inputs = processor(text=prompt, images=images, add_special_tokens=True, return_tensors="pt")
+inputs = {key: value.to(device) for key, value in inputs.items()}
 outputs = model.generate(**input_ids, max_new_tokens=100)
 print(tokenizer.decode(outputs[0]))