BSAtlas/BS-MedX · Hugging Face

description: | The BSAtlas Model is a multimodal large language model designed for advanced text generation and chatbot applications. Developed by BS|MedX, it supports both text and image inputs, or either, enabling rich contextual understanding and versatile responses. features:

Multimodal capability: Processes both text and image inputs, or either, for versatile applications.
Powered by transformers: Built using state-of-the-art transformer architectures.
High-performance inference: Optimized for tasks combining natural language understanding and image analysis.
Fine-tuned for accuracy: Based on the robust Llama 3.2 11B model, enhanced with multimodal capabilities. use_cases:
Multimodal chatbot development: Enables AI systems to process and respond based on text, image, or a combination of inputs.
Content creation: Generates descriptive text from images or augments text responses with visual context.
Healthcare applications: Supports applications like medical image analysis combined with conversational AI. model_details: developed_by: BS|MedX base_model: Llama 3.2 11B license: apache-2.0 languages_supported:
- English (en) installation: | To use this model, install the Hugging Face Transformers library and additional dependencies for image processing:

!pip install transformers pillow torch unsloth datasets
from transformers import AutoModelForCausalLM, AutoTokenizer  
from PIL import Image  

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("BSAtlas/model-name")  
model = AutoModelForCausalLM.from_pretrained("BSAtlas/model-name")  

# Example usage for text input
input_text = "Describe the contents of an image."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Example usage for multimodal input
image = Image.open("path/to/image.jpg")
image_features = model.process_image(image)  # Replace with your image processing logic
inputs = tokenizer("Analyze this image:", return_tensors="pt")
outputs = model.generate(**inputs, image_features=image_features)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))