---
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
- GRPO
- meta
license: apache-2.0
language:
- en
datasets:
- openai/gsm8k
---

<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/669777597cb32718c20d97e9/4emWK_PB-RrifIbrCUjE8.png"
     alt="Title card" 
     style="width: 500px;
            height: auto;
            object-position: center top;">
</div>

**Website - https://www.alphaai.biz**

# Uploaded  model

- **Developed by:** alphaaico
- **License:** apache-2.0
- **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
<think>
[Detailed reasoning, if required. Otherwise, this section remains empty.]
</think>
<reflection>
[Internal thought process explaining whether reasoning was needed.]
</reflection>
<answer>
[Final response.]
</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.

## Quantization Levels Available
- q4_k_m
- q5_k_m
- q8_0
- 16-bit (Full Precision)

GGUF Versions - https://huggingface.co/alpha-ai/Reason-With-Choice-3B-GGUF

## Ideal Configuration for Usage
- Temperature: 0.8
- Top-p: 0.95
- Max Tokens: 1024

## Use Cases

**Reason-With-Choice-3B is ideal for:**

- 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:
```python
from transformers import AutoModel
from safetensors.torch import save_file

model = AutoModel.from_pretrained("path/to/model")
state_dict = model.state_dict()
save_file(state_dict, "model.safetensors")
print("Model converted to safetensors successfully.")
```

Alternatively, GGUF models are available for optimized inference with llama.cpp, exllama, and other runtime frameworks.

Choose the format best suited to your security, performance, and deployment requirements.