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)

./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.

SandLogicTechnologies
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MedScholar-1.5B-GGUF