Vector-L1-4B

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. It is designed to run on modest consumer hardware so that schools and individual teachers can use it locally and offline.

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Model Summary

Developed by	MikaLabs
Model name	Vector-L1-4B
License	Apache 2.0
Language	English
Domain	K–12 / secondary education: Maths, Biology, Chemistry, Physics

Vector-L1-4B identifies itself as Vector, a teaching assistant by MikaLabs.

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Intended Use

Vector-L1-4B is intended as a teaching-assistant model for educators. It is good at:

• Differentiated worksheets — multi-tier (support / core / extension) question sets that show genuine difficulty progression.
• Mark schemes — with method marks (M) and answer marks (A) shown separately.
• Misconception guides — listing common, subject-specific student misconceptions and how to address them.
• Lesson plans — structured with objectives, starters, main activities, and plenaries.
• Mixed-format questions — short answer, true/false, fill-in-the-blank, calculation, explain-your-reasoning.
• Concept explanations — pitched to a specified age or ability level.
• Following formatting and structural instructions — e.g. "no multiple choice", "output as a markdown table", "give three tiers".

Out of Scope / Not Intended For

• High-stakes or unsupervised assessment without a human teacher reviewing the output.
• A substitute for a qualified teacher's judgement.
• General-purpose chat, coding, or non-educational tasks (it is specialised).
• Subjects outside Maths and the Sciences (coverage is weaker elsewhere).

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Strengths

Vector-L1-4B punches well above its size as a teaching assistant. It excels at:

• Differentiated worksheets with genuinely distinct support / core / extension tiers and real difficulty progression.
• Professional mark schemes that separate method marks (M) from answer marks (A), the way real exam marking works.
• Subject-specific misconception guides — identifying the actual errors students make on a topic and how to address them.
• Structured lesson plans with clear objectives, starters, main activities, and plenaries.
• A wide range of question formats — short answer, true/false with justification, fill-in-the-blank, calculation, and explain-your-reasoning — without defaulting to multiple choice.
• Strong instruction-following on complex, multi-part requests (e.g. "three tiers, a mark scheme, misconceptions, no multiple choice, output as markdown").
• Accurate level calibration, pitching difficulty appropriately for the age or ability you specify.
• Clean, ready-to-use output — it produces the resource you asked for directly, without conversational filler.

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.

How to Use

Example (transformers):

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MikaLabs/Vector-L1-4B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "user", "content": "Create a differentiated worksheet on Pythagoras' theorem for a mixed-ability class. Three tiers with 3 questions each, a mark scheme with method and answer marks, and a list of common misconceptions. No multiple choice."}
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Recommended generation settings: temperature 0.7, top_p 0.8.

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Ethical Considerations & Responsible Use

• Outputs — especially answer keys and scientific facts — must be reviewed by a qualified educator before use with students.
• It is an assistant, not an authority.
• It is specialised for English-language Maths and Science teaching; quality and accuracy degrade outside that scope.

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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}
}

Acknowledgements

Built on Qwen3-4B-Instruct-2507 by the Qwen team, used under the Apache 2.0 license.