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--- |
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license: apache-2.0 |
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language: |
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- en |
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- ar |
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- fr |
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- de |
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- hi |
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- he |
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- ru |
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new_version: BSAtlas/BS_MedX_MedChat |
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pipeline_tag: image-to-text |
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--- |
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--- |
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description: | |
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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. |
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features: |
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- Multimodal capability: Processes both text and image inputs, or either, for versatile applications. |
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- Powered by transformers: Built using state-of-the-art transformer architectures. |
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- High-performance inference: Optimized for tasks combining natural language understanding and image analysis. |
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- Fine-tuned for accuracy: Based on the robust Llama 3.2 11B model, enhanced with multimodal capabilities. |
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use_cases: |
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- Multimodal chatbot development: Enables AI systems to process and respond based on text, image, or a combination of inputs. |
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- Content creation: Generates descriptive text from images or augments text responses with visual context. |
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- Healthcare applications: Supports applications like medical image analysis combined with conversational AI. |
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model_details: |
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developed_by: BS|MedX |
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base_model: Llama 3.2 11B |
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license: apache-2.0 |
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languages_supported: |
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- English (en) |
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installation: | |
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To use this model, install the Hugging Face Transformers library and additional dependencies for image processing: |
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```bash |
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!pip install transformers pillow torch unsloth datasets |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("BSAtlas/model-name") |
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model = AutoModelForCausalLM.from_pretrained("BSAtlas/model-name") |
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# Example usage for text input |
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input_text = "Describe the contents of an image." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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# Example usage for multimodal input |
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image = Image.open("path/to/image.jpg") |
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image_features = model.process_image(image) # Replace with your image processing logic |
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inputs = tokenizer("Analyze this image:", return_tensors="pt") |
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outputs = model.generate(**inputs, image_features=image_features) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
