Update README.md

README.md

@@ -1,14 +1,100 @@
 ---
-base_model: LeroyDyer/SpydazWeb_AI_HumanAGI_002
+base_model: 
+- LeroyDyer/SpydazWeb_AI_HumanAGI_002
+- LeroyDyer/LCARS_TOP_SCORE
+- LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0
+- LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b
+- LeroyDyer/LCARS_AI_StarTrek_Computer
+- LeroyDyer/_Spydaz_Web_AI_ActionQA_Project
+- LeroyDyer/_Spydaz_Web_AI_ChatML_512K_Project
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_ReAct_Project_UltraFineTuned
+- LeroyDyer/SpyazWeb_AI_DeepMind_Project
+- LeroyDyer/SpydazWeb_AI_Swahili_Project
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_ReAct_Project
+- LeroyDyer/_Spydaz_Web_AI_MistralStar_001_Project
+- LeroyDyer/QuietStar_Project
+- LeroyDyer/Mixtral_BioMedical_7b
+- LeroyDyer/Mixtral_AI_CyberTron_Coder
+- LeroyDyer/_Spydaz_Web_AI_BIBLE_002
+- LeroyDyer/_Spydaz_Web_AI_ChatQA_Reasoning101_Project
+- LeroyDyer/SpydazWeb_AI_Text_AudioVision_Project
+- LeroyDyer/SpydazWeb_AI_HumanAI_007
+datasets:
+- neoneye/base64-decode-v2
+- neoneye/base64-encode-v1
+- VuongQuoc/Chemistry_text_to_image
+- Kamizuru00/diagram_image_to_text
+- LeroyDyer/Chemistry_text_to_image_BASE64
+- LeroyDyer/AudioCaps-Spectrograms_to_Base64
+- LeroyDyer/winogroud_text_to_imaget_BASE64
+- LeroyDyer/chart_text_to_Base64
+- LeroyDyer/diagram_image_to_text_BASE64
+- mekaneeky/salt_m2e_15_3_instruction
+- mekaneeky/SALT-languages-bible
+- xz56/react-llama
+- BeIR/hotpotqa
+- arcee-ai/agent-data
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - mistral
-- trl
+- Mistral_Star
+- Mistral_Quiet
+- Mistral
+- Mixtral
+- Question-Answer
+- Token-Classification
+- Sequence-Classification
+- SpydazWeb-AI
+- chemistry
+- biology
+- legal
+- code
+- climate
+- medical
+- LCARS_AI_StarTrek_Computer
+- text-generation-inference
+- chain-of-thought
+- tree-of-knowledge
+- forest-of-thoughts
+- visual-spacial-sketchpad
+- alpha-mind
+- knowledge-graph
+- entity-detection
+- encyclopedia
+- wikipedia
+- stack-exchange
+- Reddit
+- Cyber-series
+- MegaMind
+- Cybertron
+- SpydazWeb
+- Spydaz
+- LCARS
+- star-trek
+- mega-transformers
+- Mulit-Mega-Merge
+- Multi-Lingual
+- Afro-Centric
+- African-Model
+- Ancient-One
 license: apache-2.0
 language:
 - en
+- sw
+- ig
+- so
+- es
+- ca
+- xh
+- zu
+- ha
+- tw
+- af
+- hi
+- bm
+- su
 ---
 
 # "Success comes from defining each task in achievable steps. Every completed step is a success that brings you closer to your goal. If your steps are unreachable, failure is inevitable. Winners create more winners, while losers do the opposite. Success is a game of winners!"
@@ -37,6 +123,27 @@ This model has been trained to perform with contexts of 512k , although in train
 
 Highly trained as well as methodolgy oriented , this model has been trained on the reAct Prcess and other structured processes . hence structured outputs (json) are very highly trained as well as orchestration of other agents and tasks : the model has been trained for tools use as well as funtion use : as well as custom processes and tools : some tools do not need code either as thier implication means the model may even generate a tool or artifct to perfrom the task :
 
+## Focused Tasks:
+
+Training was task-based, with a limited number of highly specific samples (e.g., 4k samples per task) to prioritize depth over breadth.
+Tasks included interpreting spectrograms, ECG images, SMILES chemical compounds, charts, and diagrams rather than general-purpose images.
+
+### Overfitting for Baseline Embeddings:
+
+Initial heavy overfitting on large parameter stacks ensured robust embeddings, forming a strong base for subsequent fine-tuning.
+Training Techniques:
+
+### Deep Training: 
+Adjusted the entire model to create a strong foundation.
+### Shallow Training: 
+Focused on specific layers to refine task-specific capabilities.
+Attention-Head Training: Allowed specific attention heads to specialize in task-relevant features while preserving other model capacities.
+
+
+## Key Considerations for Multimodal Models
+### Context Windows:
+Larger context windows are crucial for encoding extensive Base64 strings and generating coherent outputs.
+
 ## Features :
 
 - Text to image
@@ -44,10 +151,155 @@ Highly trained as well as methodolgy oriented , this model has been trained on t
 - Image - Text 
 - Text to sound
 - Sound/Text to Text
-- Sound - Text 
+- Sound - Text
+
+# Text Vision
+
+In the development of multimodal models, different architectures may be suggested, particularly for pretraining. Vision Transformers (ViTs), for instance, have been favored in some cases because they are efficient for tasks involving image data. However, the choice of architecture often reflects the need to reduce computational overhead and leverage pre-existing efficiencies rather than a fundamental limitation of simpler architectures.
+
+A Universal Transformer for All Modalities
+A single transformer architecture can indeed handle all modalities (text, images, sound, etc.), as it is inherently a neural network capable of processing sequential data. The challenge lies not in the model's capability but in how we frame the data. With SpydazWeb models, we propose the use of Base64 encoding as a universal representation format. Here’s why:
+
+## Base64 Encoding:
+
+Base64 converts any binary data (e.g., images, sound files) into a textual format, making it compatible with transformer models trained primarily on text.
+This approach allows the model to generate or interpret images and sound directly as Base64-encoded strings, effectively leveraging its text-processing capabilities.
+
+### Base64 Encoding for Sound:
+
+Sound files (e.g., WAV, MP3, OGG) can be encoded into Base64 and processed just like text or images.
+For training and inference, prepending a MIME type tag (e.g., data:audio/wav;base64,...) allows the model to distinguish between data types and handle them appropriately.
+Advantages:
+
+The model treats all modalities uniformly, simplifying the architecture and training pipeline.
+Specific MIME types (e.g., WAV, MP3, OGG) can help the model generate outputs in the correct format.
+
+## Data MIME Tagging:
+
+Prepending MIME type tags to Base64 strings (e.g., image/png, audio/mpeg) ensures the model can interpret and reproduce data accurately.
+Outputs from the model should include these tags to maintain consistency with training inputs.
+Output Representation:
+
+During generation, the model must return the Base64-encoded representation with MIME tags, matching the original training format.
+
+### Summary: A Unified Multimodal Approach
+Using Base64 encoding for all data types allows a single transformer architecture to seamlessly handle images, sound, and text. This approach simplifies training pipelines and extends the model's capabilities while maintaining consistency and interpretability. The proposed methodologies focus on task-specific training, efficient embedding strategies, and careful prompt engineering to maximize the transformer’s potential across all modalities.
+
+To create a pipeline for encoding and decoding files (sound or images) to and from Base64, we need to account for the following:
+
+## Generalized File Handling:
+
+The functions should handle binary data since both sound and image files are binary.
+They should work with any file format (e.g., MP3, WAV, OGG for audio; JPG, PNG, BMP for images).
+Encoding and Decoding:
+
+Encoding involves converting the binary content to Base64.
+Decoding involves reversing the Base64 string back to the original binary format.
+
+
+# Base64 Encoding/Decoding Functions
+``` python
+
+import base64
+from pathlib import Path
+
+def encode_file_to_base64(input_file_path: str, output_file_path: str = None) -> str:
+    """
+    Encodes any file (image or sound) to Base64.
+    
+    Args:
+        input_file_path (str): Path to the input file.
+        output_file_path (str): Optional path to save the Base64 encoded string.
+        
+    Returns:
+        str: Base64 encoded string of the file.
+    """
+    file_path = Path(input_file_path)
+    if not file_path.is_file():
+        raise FileNotFoundError(f"File not found: {input_file_path}")
     
-Basic Prompt :
-```xml
+    # Read file in binary mode
+    with open(file_path, "rb") as file:
+        file_data = file.read()
+    
+    # Encode to Base64
+    base64_data = base64.b64encode(file_data).decode('utf-8')
+    
+    # Save to output file if specified
+    if output_file_path:
+        with open(output_file_path, "w") as output_file:
+            output_file.write(base64_data)
+    
+    return base64_data
+
+def decode_base64_to_file(base64_data: str, output_file_path: str):
+    """
+    Decodes a Base64 string back into its original binary file.
+    
+    Args:
+        base64_data (str): The Base64 encoded string.
+        output_file_path (str): Path to save the decoded file.
+    """
+    # Decode Base64 to binary data
+    file_data = base64.b64decode(base64_data)
+    
+    # Write binary data to the output file
+    with open(output_file_path, "wb") as file:
+        file.write(file_data)
+```
+
+
+# Pipeline Example: Sound Files
+``` python
+
+# Encode sound file to Base64
+encoded_sound = encode_file_to_base64("example.mp3", "example_base64.txt")
+print(f"Encoded sound file saved to example_base64.txt")
+
+# Decode Base64 back to sound file
+decode_base64_to_file(encoded_sound, "decoded_example.mp3")
+print("Decoded sound file saved as decoded_example.mp3")
+```
+# Pipeline Example: Image Files
+``` python
+
+# Encode image file to Base64
+encoded_image = encode_file_to_base64("example_image.jpg", "example_image_base64.txt")
+print(f"Encoded image file saved to example_image_base64.txt")
+
+# Decode Base64 back to image file
+decode_base64_to_file(encoded_image, "decoded_example_image.jpg")
+print("Decoded image file saved as decoded_example_image.jpg")
+```
+# Explanation of the Functions
+### Encoding Pipeline:
+
+Read the file as binary (rb mode).
+Use base64.b64encode() to encode the binary data into Base64 format.
+Save the encoded string to an optional file if required.
+
+### Decoding Pipeline:
+
+Decode the Base64 string back to binary using base64.b64decode().
+Save the binary data as the output file in its original format.
+## Notes
+These functions can handle any binary file, including sound files (MP3, WAV, OGG) and image files (JPG, PNG, BMP).
+The Base64 output can be used in text-based applications or embedded in HTML/JSON as needed.
+Ensure the input file exists, and specify the correct output path during decoding.
+This design is flexible and reusable for various file types, making it a robust solution for encoding and decoding files into Base64.
+
+
+
+
+
+# Prompt Engineering for Training:
+
+Early training involved embedding large, detailed prompts to improve the model’s depth of response and adaptability.
+Later stages refined this with smaller prompts for more concise task-specific optimization.
+
+## Basic Prompt :
+
+```pythopn
 alpaca_prompt = """
 
 ### Personality and Modus Operandi
@@ -156,3 +408,6 @@ Hey, babe ;)
 :)"""
 
 ```
+
+
+