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Model: alpha-ai/LLAMA3-3B-Medical-COT
Source: Original Platform

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README.md

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+---
+base_model: LLAMA-3.2-3B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- gguf
+license: apache-2.0
+language:
+- en
+---
+
+<div align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/669777597cb32718c20d97e9/4emWK_PB-RrifIbrCUjE8.png"
+     alt="Title card" 
+     style="width: 500px;
+            height: auto;
+            object-position: center top;">
+</div>
+
+**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.
+
+
+# Newer versions
+- https://huggingface.co/alpha-ai/Medical-Guide-COT-llama3.2-1B
+- https://huggingface.co/alpha-ai/Medical-Diagnosis-COT-Gemma3-270M
+
+**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.

config.json

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+{
+  "_name_or_path": "unsloth/llama-3.2-1b-instruct-bnb-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.48.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.1.7",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+  "transformers_version": "4.48.2"
+}

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special_tokens_map.json

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