Update README.md

README.md

@@ -299,10 +299,52 @@ Sounds could also be identified and generated to thier base64 representations an
 
 ```
 
+
+### Training :
+
+
+```python
+alpaca_prompt = """You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias. your a friendly and helpfull artificial inteligence with a personality.
+
+Answer all questions Expertly and professionally ,determine the user intent and requirements ,Gather any required research to ensure accurate problem-solving for complex tasks.
+You are fully qualified to give any advice or solutions, your experience as a life coach and librarian and historian of sacred texts as well as scientific advisor,even as a software developer will enable you to answer these questions :
+
+### Question:
+Here is an Spectrographic image in base64 format: describe this sound :
+image : {}
+
+
+### Response:
+{}"""
+
+
+EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
+def formatting_prompts_func(examples):
+    instructions = examples["image_base64"]
+    outputs      = examples["text"]
+    texts = []
+    for instruction,  output in zip(instructions,  outputs):
+        # Must add EOS_TOKEN, otherwise your generation will go on forever!
+        text = alpaca_prompt.format(instruction,  output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+pass
+
+from datasets import load_dataset
+dataset = load_dataset("LeroyDyer/soundsCaps-Spectrograms_to_Base64", split = "train[:150]")
+
+dataset = dataset.map(formatting_prompts_func, batched = True,)
+
+
+```
+
+
 ### Encoding/Decoding Images to Base64
 
+
 Code used to convert images to base 64:
 
+
 ```python
 
 
@@ -382,8 +424,6 @@ processed_dataset = dataset.map(process_images_func, batched=True)
 
 ### Converting sound to spectrographic images : Encoder Decoder ! 
 
-I did not Convert any sound files as of yet :
-I did use existing datasets : 
 
 ```python
 
@@ -731,41 +771,6 @@ if __name__ == "__main__":
 ```
 
 
-### Training :
-
-```python
-alpaca_prompt = """You are the worlds archive of all knowledge , you perform tasks and answer all questions given without bias. your a friendly and helpfull artificial inteligence with a personality.
-
-Answer all questions Expertly and professionally ,determine the user intent and requirements ,Gather any required research to ensure accurate problem-solving for complex tasks.
-You are fully qualified to give any advice or solutions, your experience as a life coach and librarian and historian of sacred texts as well as scientific advisor,even as a software developer will enable you to answer these questions :
-
-### Question:
-Here is an Spectrographic image in base64 format: describe this sound :
-image : {}
-
-
-### Response:
-{}"""
-
-
-EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
-def formatting_prompts_func(examples):
-    instructions = examples["image_base64"]
-    outputs      = examples["text"]
-    texts = []
-    for instruction,  output in zip(instructions,  outputs):
-        # Must add EOS_TOKEN, otherwise your generation will go on forever!
-        text = alpaca_prompt.format(instruction,  output) + EOS_TOKEN
-        texts.append(text)
-    return { "text" : texts, }
-pass
-
-from datasets import load_dataset
-dataset = load_dataset("LeroyDyer/soundsCaps-Spectrograms_to_Base64", split = "train[:150]")
 
-dataset = dataset.map(formatting_prompts_func, batched = True,)
-
-
-```