**Website - https://www.alphaai.biz** # Uploaded Model - LLAMA3-3B-Medical-COT - Developed by: Alpha AI - License: Apache-2.0 - Fine-tuned from model: LLAMA-3.2-3B-Instruct - This LLAMA-3.2-3B-Instruct model was fine-tuned using Unsloth and Hugging Face’s TRL library, ensuring efficient training and high-quality inference. **Overview** LLAMA3-3B-Medical-COT is a fine-tuned reasoning and medical problem-solving model built over LLAMA-3.2-3B-Instruct. The model is trained on a dataset focused on open-ended medical problems, aimed at enhancing clinical reasoning and structured problem-solving in AI systems. This dataset consists of challenging medical exam-style questions with verifiable answers, ensuring factual consistency in responses. The fine-tuning process has strengthened the model’s chain-of-thought (CoT) reasoning, allowing it to break down complex medical queries step by step while maintaining conversational fluency. Designed for on-device and local inference, the model is optimized for quick and structured reasoning, making it highly efficient for healthcare applications, academic research, and AI-driven medical support tools. **Model Details** - Model: LLAMA-3.2-3B-Instruct - Fine-tuned By: Alpha AI - Training Framework: Unsloth + Hugging Face TRL - License: Apache-2.0 - Format: GGUF (Optimized for local use) **Quantization Levels Available:** - q4_k_m - q5_k_m - q8_0 - 16-bit Precision (This) Others - https://huggingface.co/alphaaico/LLAMA3-3B-Medical-COT-GGUF **Use Cases:** - Medical Reasoning & Diagnosis Support – Assists in clinical discussions, case reviews, and problem-solving for medical professionals. - AI-Assisted Medical Learning – Enhances student learning through structured explanations and reasoning on medical exam questions. - Logical & Step-by-Step Problem Solving – Handles structured inference tasks beyond medical reasoning, making it useful in scientific research. - Conversational AI for Healthcare – Powers virtual assistants and AI-driven consultation tools with evidence-based responses. **Model Performance:** - Fine-tuned on Verified Medical Reasoning Data – Ensures step-by-step logical responses grounded in medical accuracy. - Optimized for Local Deployment – Runs efficiently on personal GPUs and edge devices without requiring cloud infrastructure. - Structured Thought Process – Breaks down complex medical questions into logical, evidence-based answers. **Limitations & Biases:** While trained on verified medical datasets, this model is not a replacement for professional medical advice and should be used as a supplementary tool rather than a definitive diagnostic system. The model may exhibit biases from its dataset, and responses should always be validated by medical experts before being used in real-world applications. **Acknowledgments** Special thanks to: - Unsloth for optimizing fine-tuning pipelines. - Hugging Face TRL for robust model training tools. - Dataset contributors for providing structured medical reasoning problems.