Finetuned from model : meta-llama/Llama-3.2-3B-Instruct
Training Framework: Unsloth + Hugging Face TRL
Finetuning Techniques: GRPO + Reward Modelling
Overview
Welcome to the next evolution of AI reasoning! Reason-With-Choice-3B is not just another fine-tuned model, it's a game-changer. It doesn't just generate reasoning, it chooses whether reasoning is even necessary before delivering an answer. This self-reflective capability allows it to introspect, analyze, and adapt to the complexity of each question, ensuring the most efficient and insightful response possible.
Think about it: most AI models blindly generate reasoning even when unnecessary, leading to bloated, redundant responses. Not this one. With its built-in decision-making, Reason-With-Choice-3B determines if deep reasoning is needed or if a direct answer will suffice—bringing unparalleled efficiency and intelligence to your AI-driven applications.
Key Highlights
Reasoning & Self-Reflection: The model first decides if reasoning is necessary and then either provides step-by-step logic or directly answers the question.
Structured Output: Responses follow a strict format with <think>, <reflection>, and <answer> sections, ensuring clarity and interpretability.
Optimized Training: Trained using GRPO (Guided Reward Policy Optimization) to enforce structured responses and improve decision-making.
Efficient Inference: Fine-tuned with Unsloth & Hugging Face's TRL, ensuring faster inference speeds and optimized resource utilization.
Prompt Structure
The model generates responses in the following structured format:
python
1<think>2[Detailed reasoning,if required. Otherwise, this section remains empty.]3</think>4<reflection>5[Internal thought process explaining whether reasoning was needed.]6</reflection>7<answer>8[Final response.]9</answer>
Key Features
Decision-Making Capability: The model intelligently determines whether reasoning is necessary before answering.
Improved Accuracy: Training with reward functions ensures adherence to logical response structure.
Structured Outputs: Guarantees that each response follows a predictable and interpretable format.
Enhanced Efficiency: Optimized inference with vLLM for fast token generation and low memory footprint.
Multi-Use Case Compatibility: Can be used for Q&A systems, logical reasoning tasks, and AI-assisted decision-making.
AI Research: Investigating decision-making and reasoning processes in AI.
Conversational AI: Enhancing chatbot intelligence with structured reasoning.
Automated Decision Support: Assisting in structured, step-by-step problem-solving.
Educational Tools: Providing logical explanations for learning and problem-solving.
Business Intelligence: AI-assisted decision-making for operational and strategic planning.
Limitations & Considerations
Domain Adaptation: May require further fine-tuning for domain-specific tasks.
Inference Time: Increased processing time when reasoning is necessary.
Potential Biases: Outputs depend on training data and may require verification for critical applications.
License
This model is released under the Apache-2.0 license.
Acknowledgments
Special thanks to the Unsloth team for optimizing the fine-tuning pipeline and to Hugging Face's TRL for enabling advanced fine-tuning techniques.
Security & Format Considerations
This model has been saved in .bin format due to Unsloth's default serialization method. If security is a concern, we recommend converting to .safetensors using:
