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- ---
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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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  - en
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- ---
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- # Uploaded model
 
 
 
 
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- - **Developed by:** alphaaico
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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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- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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.