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
```

SpydazWeb_VisonEncoderDecoder_Project / README.md

LeroyDyer

Update README.md

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	---
	library_name: transformers
	license: apache-2.0
	language:
	- en
	tags:
	- image-text-to-text
	- text-to-text
	- image-text-to-image-text
	pipeline_tag: image-text-to-text
	BaseModel:
	- Mixtral_AI_Cyber_Matrix_2.0(7b)
	Decoder:
	- Locutusque/TinyMistral-248M-v2
	ImageProcessor:
	- ikim-uk-essen/BiomedCLIP_ViT_patch16_224
	- Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12
	Encoder:
	- google/vit-base-patch16-224-in21k
	---

	# LeroyDyer/Mixtral_AI_Cyber_Q_Vision


	VisionEncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture
	with one of the base vision model classes of the library as encoder and another one as decoder
	when created with the :

	```python
	# class method for the encoder and :
	transformers.AutoModel.from_pretrained
	# class method for the decoder.
	transformers.AutoModelForCausalLM.from_pretrained


	```

	### Model Description
	This is an experiment in vision - the model has been created as a mistral/VisionEncoder/Decoder

	Customized from:

	```yaml

	BaseModel:
	- Mixtral_AI_Cyber_Matrix_2.0(7b)
	Decoder:
	- Locutusque/TinyMistral-248M-v2
	ImageProcessor:
	- ikim-uk-essen/BiomedCLIP_ViT_patch16_224
	- Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12
	Encoder:
	- google/vit-base-patch16-224-in21k



	```

	- Developed by: [LeroyDyer]
	- Model type: [image-text-to-image-text]
	- Language(s) (NLP): [English]


	## Summary

	This is the model card of a 🤗 transformers model that has been pushed on the Hub.
	Previous vision models have been 50/50 as the multimodel model actully requires a lot of memory and gpu and harddrive space to create;
	the past versions have been attempts to Merge the capabilitys into the main mistral model whilst still retaining its mistral tag!
	After reading many hugging face articles:

	The BackBone Issue is the main cause of creating multi modals !:

	with the advent of tiny models we are able to leverage the decoder abilitys as a single expert-ish... within the model :
	by reducing the size to a fully trainined tiny model!
	this will only produce decodings and not conversations so it needs to be smart and respond with defined answers: but in general it will produce captions: but as domain based it may be specialized in medical or art etc:

	The main llm still needs to retain these models within hence the back bone method of instigating a VisionEncoderDecoder model: istead of a llava model which still need wrangling to work correctly without spoiling the original transformers installation:
	Previous experiments proved that the mistral large model could be used as a decoder but the total model jumped to 13b so the when applying the tiny model it was only effected by the weight of the model 248M




	## 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
	from PIL import Image
	import torch

	processor = AutoProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
	model = VisionEncoderDecoderModel.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")

	# load image from the IAM dataset
	url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
	image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

	# training
	model.config.decoder_start_token_id = processor.tokenizer.eos_token_id
	model.config.pad_token_id = processor.tokenizer.pad_token_id
	model.config.vocab_size = model.config.decoder.vocab_size

	pixel_values = processor(image, return_tensors="pt").pixel_values
	text = "hello world"
	labels = processor.tokenizer(text, return_tensors="pt").input_ids
	outputs = model(pixel_values=pixel_values, labels=labels)
	loss = outputs.loss

	# inference (generation)
	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]

	## Training Details

	Currently inputs are raw and untrained ;

	ie: they NEED to be trained as the tensors are randomize maybe?
	despite using pretrained starting blocks. the encoder decoder modules are ready to be placed in train mode:
	The main model ie the LLM will need lora/Qlora/Peft etc:

	This model will stay in this state as a base training point ! so later versions will be trained;
	This model is fully usable and still expected to score well ;

	The small tiny mistral is also a great performer and a great block to begin a smaller experts model (later) or any multimodal project ie: its like a mini pretrined bert/llama(Mistral is a clone of llamaAlpaca!

	```python


	from transformers import ViTImageProcessor, AutoTokenizer, VisionEncoderDecoderModel
	from datasets import load_dataset

	image_processor = ViTImageProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
	tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
	model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
	"LeroyDyer/Mixtral_AI_Cyber_Q_Vision", "LeroyDyer/Mixtral_AI_Cyber_Q_Vision"
	)

	model.config.decoder_start_token_id = tokenizer.cls_token_id
	model.config.pad_token_id = tokenizer.pad_token_id

	dataset = load_dataset("huggingface/cats-image")
	image = dataset["test"]["image"][0]
	pixel_values = image_processor(image, return_tensors="pt").pixel_values

	labels = tokenizer(
	"an image of two cats chilling on a couch",
	return_tensors="pt",
	).input_ids

	# the forward function automatically creates the correct decoder_input_ids
	loss = model(pixel_values=pixel_values, labels=labels).loss



	```


	### Model Architecture


	Aha !!! Here is how you create such a model ::



	``` python

	from transformers import MistralConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel

	# Initializing a ViT & Mistral style configuration
	config_encoder = ViTConfig()
	config_decoder = MistralConfig()

	config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

	# Initializing a ViTMistral model (with random weights) from a ViT & Mistral style configurations
	model = VisionEncoderDecoderModel(config=config)

	# Accessing the model configuration
	config_encoder = model.config.encoder
	config_decoder = model.config.decoder
	# set decoder config to causal lm
	config_decoder.is_decoder = True
	config_decoder.add_cross_attention = True

	# Saving the model, including its configuration
	model.save_pretrained("my-model")

	# loading model and config from pretrained folder
	encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
	model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)


	```