Sphinx: 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!
| Metric | Value (%) |
|---|---|
| Average | 31.45 |
| IFEval (0-Shot) | 71.23 |
| BBH (3-Shot) | 49.40 |
| MATH Lvl 5 (4-Shot) | 2.72 |
| GPQA (0-shot) | 5.82 |
| MuSR (0-shot) | 13.05 |
| MMLU-PRO (5-shot) | 46.49 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
| Metric | Value (%) |
|---|---|
| Average | 31.45 |
| IFEval (0-Shot) | 71.23 |
| BBH (3-Shot) | 49.40 |
| MATH Lvl 5 (4-Shot) | 2.72 |
| GPQA (0-shot) | 5.82 |
| MuSR (0-shot) | 13.05 |
| MMLU-PRO (5-shot) | 46.49 |
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Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard71.230
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard49.400
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard2.720
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.820
- acc_norm on MuSR (0-shot)Open LLM Leaderboard13.050
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard46.490