Update README.md

README.md

@@ -22,12 +22,58 @@ This model is ideal for advanced NLP tasks, including ethical decision-making, m
 
 **Model Overview**
 
-- **Developer**: Shafaet Brady Hussain - [ResearchForum](https://researchforum.online)
-- **Funded by**: [Researchforum.online](https://researchforum.online)
-- **Language**: English
-- **Model Type**: Causal Language Model
-- **Base Model**: LLaMA 3.1 8B (Meta)
-- **License**: Apache-2.0
+SpectraMind Models
+A collection of fine-tuned Llama models optimized for CPU performance using the GGUF format. These models are designed for efficient inference with llama.cpp and other lightweight environments.
+
+Model Directory
+1. MicroSpectraMind (1B Model)
+Base Model: Fine-tuned from Llama-3.2-1B.
+Fine-Tuning Details: Explain the dataset used (e.g., domain-specific text, chat dialogues, or tasks such as summarization or question answering).
+Optimization: Quantized to both:
+f16: For maximum accuracy.
+q8_0: For reduced size and faster inference.
+Use Case: Ideal for lightweight applications such as embedded systems or single-threaded inference on CPUs.
+File Sizes:
+MicroSpectraMind_f16.gguf: 2.4 GB
+MicroSpectraMind_q8.gguf: 1.3 GB
+2. SpectraMind3 (3B Model)
+Base Model: Fine-tuned from Llama-3.2-3B.
+Fine-Tuning Details: Include key aspects of the fine-tuning, such as datasets or hyperparameters used, and what tasks it excels at.
+Optimization:
+f16: For higher accuracy.
+q8_0: For better efficiency.
+Use Case: Balances accuracy and performance, suited for general-purpose natural language tasks.
+File Sizes:
+SpectraMind3_f16.gguf: 4.7 GB
+SpectraMind3_q8.gguf: 3.4 GB
+3. SpectraMindZ (8B Model)
+Base Model: Fine-tuned from Llama-3.2-8B.
+Fine-Tuning Details: Provide specifics on dataset/task for fine-tuning.
+Optimization:
+f16: For maximum model precision.
+q8_0: For efficient deployment with minimal performance impact.
+Use Case: Best for complex tasks requiring higher reasoning or multitasking.
+Expected File Sizes:
+SpectraMindZ_f16.gguf: Approximately 12 GB
+SpectraMindZ_q8.gguf: Approximately 8 GB
+Optimization and Compatibility
+All models are converted to GGUF format using llama.cpp, making them optimized for CPU-based inference. These models are ideal for systems with limited resources, such as desktops, laptops, and embedded devices.
+Quantized versions (q8_0) are significantly smaller and faster, while maintaining reasonable accuracy.
+How to Use
+Download the GGUF Files: Use the provided links to download the .gguf files.
+Run on llama.cpp: Example command for inference:
+bash
+Copy code
+./main -m SpectraMind3_q8.gguf -p "Your prompt here"
+Choose Quantization Based on Use Case:
+Use f16 for maximum accuracy (e.g., research or high-precision tasks).
+Use q8_0 for faster inference (e.g., real-time applications).
+Model Comparison
+Model	Parameters	f16 Size	q8_0 Size	Use Case
+MicroSpectraMind	1B	2.4 GB	1.3 GB	Lightweight, quick responses
+SpectraMind3	3B	4.7 GB	3.4 GB	Balanced accuracy/performance
+SpectraMindZ	8B	12 GB	8 GB	Advanced tasks, complex reasoning
+
 
 **Usage**: Run on any web interface or as a bot for self-hosted solutions. Designed to run smoothly on CPU.