Update README.md

README.md

@@ -121,6 +121,61 @@ model-index:
 This model was converted to GGUF format from [`Daemontatox/Sphinx2.0`](https://huggingface.co/Daemontatox/Sphinx2.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Daemontatox/Sphinx2.0) for more details on the model.
 
+---
+Model details:
+-
+phinx: The Apex of Logical Deduction and Chain-of-Thought Reasoning
+
+    Developed by: Daemontatox
+    License: Apache-2.0
+    Base Model: Fine-tuned from unsloth/qwen2.5-14b-instruct-bnb-4bit
+    Accelerated by: Unsloth Framework
+    TRL-Optimized: Integrated with Huggingface's TRL library for enhanced performance in logical reasoning.
+
+Unveiling Sphinx: Master of Reasoned Thought
+
+Sphinx is a cutting-edge Long Chain-of-Thought (CoT) reasoning model meticulously crafted to unravel complex challenges requiring rigorous logical analysis. Built upon the robust foundation of the Qwen2.5 architecture, Sphinx excels at constructing coherent, step-by-step thought processes, providing unparalleled insight into its reasoning and ensuring clarity in its conclusions.
+
+    "Where complexity yields to logical clarity."
+
+Core Strengths: Reasoning, Logic, and CoT
+
+    Unrivaled Chain-of-Thought (CoT) Mastery: Engineered for dissecting intricate problems, Sphinx meticulously constructs each step of its reasoning, offering a transparent and verifiable pathway to the solution.
+    Deep Logical Reasoning Capabilities: Sphinx is adept at navigating complex logical structures, drawing valid inferences and forming sound conclusions through multi-layered analysis.
+    Exceptional Reasoning Fidelity: Fine-tuned to maintain the highest standards of logical consistency, Sphinx delivers outputs that are not only correct but also demonstrably well-reasoned.
+    Efficient Long-Context Reasoning: Leveraging the power of Unsloth, Sphinx processes extensive information efficiently, maintaining logical coherence across extended reasoning chains.
+    Explainable AI through Transparent Logic: Sphinx's inherent CoT approach provides explicit and understandable reasoning, making its decision-making process transparent and trustworthy.
+
+Model Architecture and Fine-tuning for Logical Prowess
+Architectural Foundation
+
+    Base Model: Qwen2.5-14B - Renowned for its strong general language understanding, forming a solid basis for specialized reasoning.
+    Parameters: 14 billion - Providing the capacity to model intricate reasoning patterns.
+    Quantization: 4-bit precision using BitsAndBytes (bnb) - Optimizing for accessibility without sacrificing logical reasoning accuracy.
+    Extended Reasoning Window: Supports inputs up to 16k tokens, crucial for accommodating the detailed context required for complex logical deductions.
+
+Training Methodology: Honing Logical Acumen
+
+    Frameworks: Huggingface Transformers + TRL + Unsloth - A powerful combination for efficient training and reinforcement learning.
+    Data Sources: A meticulously curated collection of datasets specifically designed to challenge and refine logical reasoning skills, encompassing academic, legal, and formal logic domains.
+    Optimization Strategies:
+        LoRA (Low-Rank Adaptation): Enabling parameter-efficient fine-tuning, focusing on adapting the model for superior logical inference.
+        Reinforcement Learning from Human Feedback (RLHF): Guiding the model towards generating more logically sound and human-aligned reasoning steps.
+
+Sphinx's Reasoning Toolkit: Capabilities in Action
+
+    Masterful Long-CoT Generation: Deconstructs and conquers multi-layered problems by constructing detailed, logically interconnected reasoning sequences.
+    Explanatory Power through Logic: Provides clear, step-by-step logical derivations for its outputs, enhancing trust and understanding.
+    Adaptable Logical Framework: Easily tailored to specialized reasoning tasks through targeted fine-tuning, enabling application in diverse logical domains.
+
+Unlocking Potential: Applications Driven by Logic
+
+    Advanced Academic Research: Generating in-depth, logically structured analyses for complex scientific and philosophical inquiries.
+    Robust Legal Reasoning Assistance: Constructing and articulating multi-step legal arguments with precision and logical rigor.
+    Transformative STEM Education: Guiding learners through intricate mathematical and logical problems with clear, step-by-step explanations.
+    Transparent Cognitive AI Systems: Powering AI systems where explainability and logical justification are paramount for decision-making.# Open LLM Leaderboard Evaluation Results Detailed results can be found here! Summarized results can be found here!
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)