Update README.md

README.md

@@ -36,25 +36,6 @@ transformers.AutoModelForCausalLM.from_pretrained
 ```
 
 ### Model Description
-
-
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. 
-Previous vision models have been 50/50 as the multimodel model actully requires a lot of memory and gpu and harddrive space to create;
-the past versions have been attempts to Merge the capabilitys into the main mistral model whilst still retaining its mistral tag!
-After reading many hugging face articles: 
-
-The BackBone Issue is the main cause of creating multi modals !:
-
-with the advent of tiny models we are able to leverage the decoder abilitys as a single expert-ish... within the model :
-by reducing the size to a fully trainined tiny model!
-this will only produce decodings and not conversations so it needs to be smart and respond with defined answers: but in general it will produce captions: but as domain based it may be specialized in medical or art etc:
-
-The main llm still needs to retain these models within hence the back bone method of instigating a VisionEncoderDecoder model: istead of a llava model which still need wrangling to work correctly without spoiling the original transformers installation:
-Previous experiments proved that the mistral large model could be used as a decoder but the total model jumped to 13b so the when applying the tiny model it was only effected by the weight of the model 248M
-
-
-
 This is an experiment in vision - the model has been created as a mistral/VisionEncoder/Decoder 
 
 Customized from: 
@@ -80,6 +61,25 @@ Encoder:
 - **Language(s) (NLP):** [English]
 
 
+## Summary
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. 
+Previous vision models have been 50/50 as the multimodel model actully requires a lot of memory and gpu and harddrive space to create;
+the past versions have been attempts to Merge the capabilitys into the main mistral model whilst still retaining its mistral tag!
+After reading many hugging face articles: 
+
+The BackBone Issue is the main cause of creating multi modals !:
+
+with the advent of tiny models we are able to leverage the decoder abilitys as a single expert-ish... within the model :
+by reducing the size to a fully trainined tiny model!
+this will only produce decodings and not conversations so it needs to be smart and respond with defined answers: but in general it will produce captions: but as domain based it may be specialized in medical or art etc:
+
+The main llm still needs to retain these models within hence the back bone method of instigating a VisionEncoderDecoder model: istead of a llava model which still need wrangling to work correctly without spoiling the original transformers installation:
+Previous experiments proved that the mistral large model could be used as a decoder but the total model jumped to 13b so the when applying the tiny model it was only effected by the weight of the model 248M
+
+
+
+
 ## How to Get Started with the Model
 
 
@@ -168,7 +168,12 @@ loss = model(pixel_values=pixel_values, labels=labels).loss
 ```
 
 
-### Model Architecture and Objective
+### Model Architecture 
+
+
+Aha !!! Here is how you create such a model :: 
+
+
 
 ``` python