Instructions to use budecosystem/genz-mm-vt-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/genz-mm-vt-7b with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/genz-mm-vt-7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("budecosystem/genz-mm-vt-7b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-mm-vt-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use budecosystem/genz-mm-vt-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/genz-mm-vt-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/genz-mm-vt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/budecosystem/genz-mm-vt-7b

SGLang

How to use budecosystem/genz-mm-vt-7b 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 "budecosystem/genz-mm-vt-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/genz-mm-vt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "budecosystem/genz-mm-vt-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/genz-mm-vt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use budecosystem/genz-mm-vt-7b with Docker Model Runner:
```
docker model run hf.co/budecosystem/genz-mm-vt-7b
```

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GenZ Vision Assistant

Welcome to the home of GenZ Vision Assistant, an advanced multimodal AI model fine-tuned to understand text and visual inputs to provide contextually relevant responses.

Our dedicated team at Bud Ecosystem believes in the power of fusion – the fusion of textual and visual information, to create AI models that understand the world more like humans do. This belief led us to develop GenZ Vision Assistant, a model that combines the capabilities of language understanding with image interpretation.

From image captioning and visual question answering to multimodal translation, GenZ Vision Assistant opens up a realm of possibilities. It's not just about understanding text or images, it's about understanding them together, in context, to provide meaningful, accurate, and holistic responses.

We invite you to join us in this exciting journey as we continue to evolve GenZ Vision Assistant and explore the untapped potential of multimodal AI models.

Project Updates 📢

Model uploaded to HuggingFace 🚀
Inference code (Coming soon) ⏳
Training details (Coming soon) ⏳

Stay tuned for more updates as we continue to refine and expand GenZ Vision Assistant. Together, let's redefine what's possible with AI! 👨‍💻👩‍💻

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