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README.md
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base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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license: apache-2.0
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language:
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---
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# Uploaded
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit
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base_model: LLAMA-3.2-1B-Instruct
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tags:
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- text-generation-inference
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- transformers
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license: apache-2.0
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language:
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- en
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# Uploaded Model - LLAMA3-3B-Medical-COT
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- Developed by: Alpha AI
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- License: Apache-2.0
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- Fine-tuned from model: LLAMA-3.2-1B-Instruct
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- 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.
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**Overview**
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AlphaAI-Happy-Reasoner-Zero 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.
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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.
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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.
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**Model Details**
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- Model: LLAMA-3.2-1B-Instruct
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- Fine-tuned By: Alpha AI
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- Training Framework: Unsloth + Hugging Face TRL
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- License: Apache-2.0
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- Format: GGUF (Optimized for local use)
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**Quantization Levels Available:**
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- q4_k_m
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- q5_k_m
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- q8_0
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- 16-bit Precision (https://huggingface.co/alphaaico/LLAMA3-3B-Medical-COT)
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**Use Cases:**
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- Medical Reasoning & Diagnosis Support – Assists in clinical discussions, case reviews, and problem-solving for medical professionals.
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- AI-Assisted Medical Learning – Enhances student learning through structured explanations and reasoning on medical exam questions.
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- Logical & Step-by-Step Problem Solving – Handles structured inference tasks beyond medical reasoning, making it useful in scientific research.
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- Conversational AI for Healthcare – Powers virtual assistants and AI-driven consultation tools with evidence-based responses.
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**Model Performance:**
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- Fine-tuned on Verified Medical Reasoning Data – Ensures step-by-step logical responses grounded in medical accuracy.
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- Optimized for Local Deployment – Runs efficiently on personal GPUs and edge devices without requiring cloud infrastructure.
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- Structured Thought Process – Breaks down complex medical questions into logical, evidence-based answers.
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**Limitations & Biases:**
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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.
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The model may exhibit biases from its dataset, and responses should always be validated by medical experts before being used in real-world applications.
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**Acknowledgments**
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Special thanks to:
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- Unsloth for optimizing fine-tuning pipelines.
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- Hugging Face TRL for robust model training tools.
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- Dataset contributors for providing structured medical reasoning problems.
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