Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- yasserrmd/MedScholar-1.5B
+pipeline_tag: text-generation
+tags:
+- Med-scholar
+- medical
+- text-generation-inference
+- quantized
+---
+
+# Quantized MedScholar-1.5B
+
+This repository provides quantized GGUF versions of the yasserrmd/MedScholar-1.5B. These 4-bit and 5-bit quantized variants retain the original model’s strengths in multimodal medical reasoning, while reducing memory and compute requirements—ideal for efficient inference on resource-constrained devices.
+
+## Model Overview
+
+- **Original Model**: yasserrmd/MedScholar-1.5B
+- **Quantized Versions**: 
+  - Q4_K_M (4-bit quantization)
+  - Q5_K_M (5-bit quantization)
+- **Architecture**: Decoder-only transformer
+- **Base Model**: Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
+- **Training Framework**: Unsloth + QLoRA
+- **Dataset**: MIRIAD-4.4M (1M samples) [ODC-By 1.0]
+- **License**: Apache-2.0 (inherits from base model); dataset is ODC-By 1.0
+- **Language**: English
+
+## Quantization Details
+
+### Q4_K_M Version
+- Approx. ~68% size reduction
+- Lower memory footprint (~940 MB)
+- Best suited for deployment on edge devices or low-resource GPUs
+- Slight performance degradation in complex reasoning scenarios
+
+### Q5_K_M Version
+- Approx. ~64% size reduction
+- Higher fidelity (~1.04 GB)
+- Better performance retention, recommended when quality is a priority
+
+### Usage
+Below, there are some code snippets on how to get quickly started with running the model.
+**llama.cpp (text-only)**
+```sh
+./llama-cli -hf SandLogicTechnologies/MedScholar-1.5B-GGUF -p "What are the symptoms of diabetes"
+```
+
+### Note
+
+⚠️ This model is for research, educational, and exploration purposes only. It is not a medical device and must not be used to provide clinical advice, diagnosis, or treatment.
+
+
+---
+
+
+## Acknowledgments
+
+- These quantized models are based on the original work by the [https://huggingface.co/yasserrmd/MedScholar-1.5B].
+- MIRIAD Dataset by Zheng et al. (2025) – [https://huggingface.co/datasets/miriad/miriad-4.4M].
+- Qwen2.5 by Alibaba - [https://huggingface.co/Qwen].
+- Training infrastructure: [https://github.com/unslothai/unsloth].
+
+---
+
+## Contact
+For any inquiries or support, please contact us at [email protected] or visit our [Website](https://www.sandlogic.com/).
+
+