Clyrai_Vortex_Reasoning

Clyrai Vortex Reasoning

• Developed by: clyrai
• License: apache-2.0
• Fine-tuned from: saishshinde15/Clyrai_Base_Reasoning
• Category: Experimental, Research

Introduction

TethysAI Vortex Reasoning is an experimental model that advances the structured reasoning capabilities pioneered by Clyrai_Base Reasoning. While the Base Reasoning model utilized Generalized Reinforced Policy Optimization (GRPO) to enhance step-by-step logical thought processes similar to DeepSeek-R1, this model takes a different approach—eliminating GRPO and instead relying on high-end Supervised Fine-Tuning (SFT) techniques.

The core objective was to investigate whether deep reasoning and self-questioning behavior could emerge purely through SFT on high-quality datasets. The results were highly promising: the model successfully questions itself internally, improves reasoning depth, and consistently generates structured, logical responses.

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Key Features

1️⃣ Advanced Reasoning Without GRPO

This model does not rely on GRPO yet achieves similar self-reflective thought processes, proving that structured reasoning can be induced through high-quality SFT alone.

2️⃣ Self-Questioning and Iterative Thinking

The model actively asks itself intermediate questions before answering, mimicking the deep reflection-based thought process of models like DeepSeek-R1. This leads to more reliable and well-structured responses.

3️⃣ High-Quality SFT on a Curated Dataset

To compensate for the lack of reinforcement learning, we used an extensive dataset tailored for deep reasoning. This dataset includes:

• Mathematical proofs & logical puzzles
• Complex multi-step problem-solving tasks
• Philosophical and ethical reasoning
• Scientific hypothesis evaluation

4️⃣ Implicit Use of <think> and <answer> Tokens

The model internally uses special reasoning markers (<think> and <answer>) to structure its responses, though these may not always be visible in the final output. This ensures a consistent and methodical approach to answering questions.

5️⃣ Part of the TethysAI Vortex Family

This model belongs to the Clyrai Vortex series, a collection of fine-tuned models pushing the boundaries of SFT-based reasoning without reinforcement learning.

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Breakthrough Insights

Key Takeaway: This experiment confirms that reinforcement learning is not the only pathway to advanced reasoning capabilities—with the right dataset and SFT strategies, models can self-reflect and logically deduce answers in a structured manner.

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How to Use

Running with Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model & tokenizer
model_name = "saishshinde15/Clyrai_Vortex_Reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

# Prepare input prompt
messages = [
    {"role": "system", "content": "You are an advanced AI assistant. Provide answers in a clear, step-by-step manner."},
    {"role": "user", "content": "If x + 3 = 10, what is x?"}
]

# Apply chat template and tokenize
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

# Generate response
outputs = model.generate(input_ids, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)