metadata

base_model:
  - saishshinde15/TBH.AI_Base_Reasoning
tags:
  - text-generation
  - transformers
  - causal-lm
  - reasoning
  - sft
  - gguf
license: apache-2.0
language:
  - en

TBH.AI Vortex Reasoning (GGUF)

Model Name: saishshinde15/TBH.AI_Vortex_Reasoning_GGUF
Developed by: TBH.AI
License: Apache 2.0
Fine-tuned from: TBH.AI_Base_Reasoning
Available in: 16-bit and 4-bit GGUF formats

Overview

TethysAI Vortex Reasoning is an experimental model designed to replicate the advanced reasoning abilities of TBH.AI_Base_Reasoning, which was originally enhanced using GRPO. Instead of GRPO, this model was fine-tuned with high-quality structured data using high-end Supervised Fine-Tuning (SFT) to replicate the step-by-step thinking and self-questioning mechanisms seen in models like DeepSeek-R1.

This model has been optimized for efficient inference in GGUF format, allowing for deployment on CPU-based systems and lightweight edge devices without sacrificing reasoning capabilities.

Why This Model Stands Out

🔹 Advanced Self-Reasoning:

The model questions itself internally before arriving at an answer.
Similar to DeepSeek-R1, it follows a structured reasoning process.
Uses and tokens internally, though they may not always be explicitly visible in responses.

🔹 No GRPO, Only High-End SFT:

Instead of GRPO, the model learns structured reasoning directly from fine-tuned data.
Demonstrates logical breakdowns, multi-step problem-solving, and contextual understanding.
Achieves results comparable to the base model without reinforcement learning.

🔹 Optimized for GGUF Inference:

Available in both 16-bit and 4-bit GGUF, enabling fast and memory-efficient execution on CPUs.
Ideal for on-device deployment, including edge computing, embedded AI, and AI assistants.

Usage

Use the below prompt for the best results:

You are an advanced AI assistant. Provide answers in a clear, step-by-step manner.