GGUF quantizations of Vector-L1-4B — for running locally with Ollama, LM Studio, llama.cpp, and other local AI runners.
Vector-L1-4B is an open language model built by MikaLabs to help teachers create classroom materials — differentiated worksheets, lesson plans, quizzes, mark schemes, misconception guides, and tailored explanations across Maths and the Sciences.
The "L1" denotes Light, version 1 — the first and smallest member of a planned Vector model family, designed to run on everyday school hardware so teachers can use it locally and offline.
Available Quantizations
File
Quant
Size
Notes
Vector-L1-4B-Q4_K_M.gguf
Q4_K_M
~4.8 GB
Recommended. Best balance of quality and size for most machines.
Q4_K_M offers near-full-quality output while staying small enough to run comfortably on modest hardware (a 16 GB GPU runs it with ease; it also runs on CPU with enough system RAM).
Quick Start
Ollama
Option A — run directly from the Ollama library (recommended):
This is the easiest way to use Vector. The model comes pre-configured with the correct chat template, stop tokens, recommended settings, and system prompt — nothing else to set up.
Option B — build from the GGUF file yourself. Download Vector-L1-4B-Q4_K_M.gguf from this repository, then create a file named Modelfile next to it:
FROM ./Vector-L1-4B-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>
"""
SYSTEM "You are Vector, a teaching assistant made by MikaLabs that helps educators create worksheets, lesson plans, quizzes, mark schemes, and explanations. You focus on teaching and education."
Then build and run:
bash
1ollama create vector-l1 -f Modelfile
2ollama run vector-l1
Note: The Modelfile's explicit template and stop tokens are what ensure clean, single-turn responses. Use Option A or B rather than pulling the raw .gguf without a Modelfile.
LM Studio
Download the .gguf file, place it in your LM Studio models folder (or use the in-app downloader), select it, and chat. Set temperature to 0.7 and top_p to 0.8.
llama.cpp
./llama-cli -m Vector-L1-4B-Q4_K_M.gguf -p "Create a differentiated worksheet on Pythagoras' theorem with three tiers, a mark scheme, and common misconceptions. No multiple choice." --temp 0.7 --top-p 0.8
What It's Good At
Vector-L1-4B punches well above its size as a teaching assistant. It excels at:
Differentiated worksheets with genuinely distinct support / core / extension tiers.
Professional mark schemes that separate method marks (M) from answer marks (A).
Subject-specific misconception guides — the actual errors students make, and how to address them.
Structured lesson plans with objectives, starters, main activities, and plenaries.
A wide range of question formats — short answer, true/false, fill-in-the-blank, calculation, explain-your-reasoning — without defaulting to multiple choice.
Strong instruction-following on complex, multi-part requests.
Accurate level calibration for the age or ability you specify.
Clean, ready-to-use output — the resource you asked for, with no filler.
It identifies itself as Vector, a teaching assistant by MikaLabs.
A Note on Scale
Vector-L1-4B is a compact 4-billion-parameter model designed to run on everyday school hardware. It is built for school and secondary-level teaching, not university or research-level material. On very hard problems it may occasionally make mistakes, so — as with any AI tool — answer keys and factual content should be reviewed by a teacher before use with students.
Recommended Settings
Temperature: 0.7
Top-p: 0.8
License
Apache 2.0. Built on Qwen3-4B-Instruct-2507 by the Qwen team, used under the Apache 2.0 license.
Citation
@misc{vector-l1-4b,
title = {Vector-L1-4B: An Open Teaching-Assistant Model},
author = {MikaLabs},
year = {2026},
url = {https://huggingface.co/MikaLabs/Vector-L1-4B}
}
