Update README.md

README.md

@@ -1,16 +1,52 @@
 ---
+name: BSAtlas Model
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- chat-bot
-- llm
+  - image-to-text
+  - transformers
+  - unsloth
+  - chat-bot
+  - mllm
 license: apache-2.0
 language:
-- en
----
+  - en
+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:
+  ```bash
+  !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))
 
-# Uploaded  model
+# 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))
 
-- **Developed by:** BSAtlas
-- **License:** apache-2.0