Geog-Q4_K_M

Geog-Q4_K_M is a GGUF-format language model package intended for geography study and question-answering experiments. The project is designed around a SmolLM2-based workflow and geography-focused training data.

The model is packaged for llama.cpp-compatible runtimes and can be used locally with tools such as llama-cli, LM Studio, Ollama-compatible GGUF loaders, or other GGUF-supported inference apps.

Model Details

• Model name: Geog-Q4_K_M
• Format: GGUF
• Quantization target: Q4_K_M
• Base workflow: SmolLM2-style causal language model workflow
• Domain: Geography
• Language: English
• Runtime target: llama.cpp-compatible inference

Model Statistics

Attribute	Value
Model Name	Geog-Q4_K_M
Base Model	SmolLM2
Parameters	135M
Domain	Geography
Language	English
Model Format	GGUF
Quantization	Q4_K_M
Training Samples	1,000
Dataset Format	JSON
Dataset Files	10
Average Samples per File	100
Dataset Schema	8 Structured Fields
Supported Topics	Earth, Climate, Maps, Latitude & Longitude, Physical Geography, Indian Geography, Natural Resources, and more
Inference Runtime	llama.cpp Compatible

Usage

llama-cli -m Geog-Q4_K_M.gguf -p "Explain latitude and longitude in simple words."

Example prompts:

What is the difference between weather and climate?
Explain the formation of monsoon winds.
Give a short note on plate tectonics.
Create 5 geography MCQs about rivers.

Training data

This project is set up around a custom geography dataset containing 1,000 structured records.

Each record includes structured fields such as:

• category
• section
• topic
• context
• question
• answer
• difficulty
• keywords

The dataset covers multiple geography areas, including topics such as Earth, latitude and longitude, physical geography, climate, natural resources, maps, Indian geography, and general geography study concepts.

Intended Use

This model package is intended for:

• Geography study assistance
• Short factual explanations
• Topic summaries
• Basic question-answering experiments
• Local GGUF inference testing

Limitations

• The model may produce incorrect or incomplete information.
• The dataset is small compared with large-scale pretraining corpora.
• Outputs should be verified before use in exams, teaching material, or decision-making.
• This model is not a replacement for authoritative geography textbooks, atlases, government sources, or academic references.

Notes

This repository contains the upload-ready GGUF package and model card. The local project contains the scripts used to prepare data, train/export, and package the model.

Check and respect the license of any training data before publishing derived models.