alpha-ai
/

Reason-With-Choice-3B

@@ -1,202 +1,117 @@
 ---
-library_name: transformers
 tags:
 - unsloth
-- trl
-- grpo
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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 ---
+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>
+# 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)
+## 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.