Triangle104/Sphinx2.0-Q5_K_S-GGUF

This model was converted to GGUF format from Daemontatox/Sphinx2.0 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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!

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Sphinx2.0-Q5_K_S-GGUF --hf-file sphinx2.0-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Sphinx2.0-Q5_K_S-GGUF --hf-file sphinx2.0-q5_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Sphinx2.0-Q5_K_S-GGUF --hf-file sphinx2.0-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Sphinx2.0-Q5_K_S-GGUF --hf-file sphinx2.0-q5_k_s.gguf -c 2048