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:
text
1What is the difference between weather and climate?
2Explain the formation of monsoon winds.
3Give a short note on plate tectonics.
4Create 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.
