Update README.md

README.md

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 ## Model Summary
 
-We introduce Cephalo, a series of multimodal materials science focused vision large language models (V-LLMs) designed to integrate visual and linguistic data for advanced understanding and interaction. 
+Cephalo is a series of multimodal materials science focused vision large language models (V-LLMs) designed to integrate visual and linguistic data for advanced understanding and interaction in human-AI or multi-agent AI frameworks. 
 
-A novel aspect of Cephalo's development is the innovative dataset generation method. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training.
+A novel aspect of Cephalo's development is the innovative dataset generation method. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training. 
 
 Cephalo can interpret complex visual scenes and generating contextually accurate language descriptions and answer queries. 
 
-The model is developed to process diverse inputs, including images and text, facilitating a broad range of applications such as image captioning, visual question answering, and multimodal content generation. 
+The model is developed to process diverse inputs, including images and text, facilitating a broad range of applications such as image captioning, visual question answering, and multimodal content generation. The architecture combines a vision encoder model and an autoregressive transformer to process complex natural language understanding. 
 
-The architecture combines a vision encoder model and an autoregressive transformer to process complex natural language understanding. 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/FsdGboq7FinNHOJnwYfWu.png)
 
 Cephalo provides a robust framework for multimodal interaction and understanding, including the development of complex generative pipelines to create 2D and 3D renderings of material microstructures as input for additive manufacturing methods.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/FsdGboq7FinNHOJnwYfWu.png)
-
 This version of Cephalo, lamm-mit/Cephalo-Phi-3-vision-128k, is based on the Phi-3-Vision-128K-Instruct model. Further details, see: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct. 
 
 ### Chat Format
@@ -87,4 +85,12 @@ generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
 response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 
 
 print(response) 
-```
+```
+
+## Dataset generation
+
+The schematic below shows a visualization of the approach to generate datasets for training the vision model. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training.
+
+
+Reproductions of two representative pages of the scientific article (here, Spivak, Buehler, et al., 2011).
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/oI-RdExZ0iHhzY5RnAH6d.png)