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---
base_model: LLAMA-3.2-1B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# Uploaded Model - LLAMA3-3B-Medical-COT
- Developed by: Alpha AI
- License: Apache-2.0
- Fine-tuned from model: LLAMA-3.2-1B-Instruct
- This LLAMA-3.2-1B-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-3.2-1B-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-3.2-1B-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 (https://huggingface.co/alphaaico/LLAMA3-3B-Medical-COT)
**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. |