Update README.md

README.md

@@ -1,5 +1,4 @@
----
-base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
+base_model: LLAMA-3.2-1B-Instruct
 tags:
 - text-generation-inference
 - transformers
@@ -9,14 +8,52 @@ tags:
 license: apache-2.0
 language:
 - en
----
 
-# Uploaded  model
+# 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.
 
-- **Developed by:** alphaaico
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit
+**Overview**
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+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.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+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.