Instructions to use LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project with Transformers:

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

pipe = pipeline("image-text-to-text", model="LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project")
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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project with vLLM:

Install from pip and serve model

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

SGLang

How to use LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project 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 "LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project" \
    --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": "LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project",
		"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 "LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project" \
        --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": "LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project",
		"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 LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/SpydazWeb_VisonEncoderDecoder_Project
```

LeroyDyer commited on Apr 8, 2024

Commit

15519b0

verified ·

1 Parent(s): df24a60

Update README.md

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README.md +10 -0

README.md CHANGED Viewed

@@ -82,6 +82,13 @@ Encoder:
 ## How to Get Started with the Model
 ```python
 from transformers import AutoProcessor, VisionEncoderDecoderModel
 import requests
@@ -110,6 +117,9 @@ loss = outputs.loss
 generated_ids = model.generate(pixel_values)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 [More Information Needed]

 ## How to Get Started with the Model
+### VisionEncoderDecoderModel
+#### As a vision encoder model :
+the tensors are combined into the original mistral model so it can be accessed by intaciating the correct model which is the VisionEncoderDecoderModel
 ```python
 from transformers import AutoProcessor, VisionEncoderDecoderModel
 import requests
 generated_ids = model.generate(pixel_values)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
+### As a standard LLM:
+it can still also be used as a normal AutoModelForCausalLM or MistralModelForCausalLM !
 [More Information Needed]