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Merlyn-education-teacher-assistant is a 12b parameter decoder-style transformer model for the education domain. It is fine-tuned from a pythia-12b base-model.

This model was trained by Merlyn Mind.

Merlyn-education-teacher-assistant is part of the family of Merlyn Mind models designed specifically for use in in- and out-of-classroom education.

Merlyn-education-teacher-assistant makes helpful recommendations based on the ongoing classroom discussion, suggesting research activities and topics for further exploration.

Model Date

June 26, 2023

Model License




At full precision the model needs > 48G GPU memory. A single A100-80GB GPU suffices, for example. If you're running on smaller GPUs, you need an instance with multiple GPUs and/or reduced model precision (e.g. use model.half() before moving to device)

Loading model and tokenizer:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "MerlynMind/merlyn-education-teacher-assistant"
device = torch.device("cuda:0") # change device id as necessary
model = AutoModelForCausalLM.from_pretrained(model_path)    
tokenizer = AutoTokenizer.from_pretrained(model_path, fast_tokenizer=True)
model.to(device) # move to device

Prompt example:

conversation = ''''user1':\tHow do some gases help keep the Earth warm?
'user2':\tSome gases, called greenhouse gases, act like a blanket around Earth by trapping heat from the sun in the atmosphere, which keeps our planet warm. This process is known as the greenhouse effect.
'user1':\tHow can we reduce greenhouse gas emissions?
'user2':\tWe can reduce greenhouse gas emissions by using renewable energy sources, increasing energy efficiency, and reducing waste.'''

prompt = tokenizer.bos_token
prompt += '''Instruction:\tYou are teaching high school students.
Instruction:\tYou are observing the following conversation between two users.
Instruction:\tGenerate 3 research activities based on the conversation.
Instruction:\tThe research activities should be doable by high school students.
Instruction:\tYour response should be a well-formed JSON array of 3 objects, each with a 'title' property and an 'activity' property.

Conversation:''' + f"\n{conversation}" + " Response:"


inputs = tokenizer(prompt, return_tensors="pt").to(device)
generate_ids = model.generate(
response = tokenizer.decode(generate_ids[0],

Example output (after response processing):

{"title": "Understanding the Greenhouse Effect", "activity": "Research the greenhouse effect and the role of greenhouse gases in keeping Earth warm. Create a presentation or poster explaining the greenhouse effect and how greenhouse gases act as a blanket around Earth."},
{"title": "Renewable Energy Sources", "activity": "Identify different renewable energy sources, such as solar, wind, and geothermal energy, and explain how they can help reduce greenhouse gas emissions."},
{"title": "Energy Efficiency and Waste Reduction", "activity": "Research energy efficiency and waste reduction practices, and develop a plan to implement these practices in your school or community to reduce greenhouse gas emissions."}


To cite this model, please use:

    author    = {Merlyn Mind AI Team},
    title     = {Merlyn Mind's education-domain language models},
    year      = {2023},
    url       = {https://www.merlyn.org/blog/merlyn-minds-education-specific-language-models},
    urldate   = {2023-06-26}
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