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--- |
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license: apache-2.0 |
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tags: |
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- MerlynMind |
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- education |
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--- |
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# Merlyn-education-teacher-assistant |
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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](https://huggingface.co/EleutherAI/pythia-12b) base-model. |
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This model was trained by [Merlyn Mind](https://www.merlyn.org/). |
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Merlyn-education-teacher-assistant is part of the family of Merlyn Mind models designed specifically for use in in- and out-of-classroom education. |
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Merlyn-education-teacher-assistant makes helpful recommendations based on the ongoing classroom discussion, suggesting research activities and topics for further exploration. |
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## Model Date |
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June 26, 2023 |
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## Model License |
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Apache-2.0 |
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## Documentation |
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* [Merlyn Mind’s education-specific language models](https://www.merlyn.org/) |
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## Usage |
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Loading model and tokenizer: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_path = "MerlynMind/merlyn-education-teacher-assistant" |
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device = torch.device("cuda:0") # change device id as necessary |
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model = AutoModelForCausalLM.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, fast_tokenizer=True) |
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model.to(device) # move to device |
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``` |
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Prompt example: |
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```python |
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conversation = ''''user1':\tHow do some gases help keep the Earth warm? |
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'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. |
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'user1':\tHow can we reduce greenhouse gas emissions? |
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'user2':\tWe can reduce greenhouse gas emissions by using renewable energy sources, increasing energy efficiency, and reducing waste.''' |
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prompt = tokenizer.bos_token |
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prompt += '''Instruction:\tYou are teaching high school students. |
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Instruction:\tYou are observing the following conversation between two users. |
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Instruction:\tGenerate 3 research activities based on the conversation. |
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Instruction:\tThe research activities should be doable by high school students. |
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Instruction:\tYour response should be a well-formed JSON array of 3 objects, each with a 'title' property and an 'activity' property. |
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Conversation:''' + f"\n{conversation}" + " Response:" |
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``` |
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Inference: |
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```python |
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inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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generate_ids = model.generate( |
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**inputs, |
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max_new_tokens=1024, |
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temperature=0.0, |
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num_beams=2 |
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) |
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response = tokenizer.decode(generate_ids[0], |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=True) |
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``` |
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Example output (after response processing): |
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```json |
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[ |
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{"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."}, |
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{"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."}, |
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{"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."} |
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] |
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``` |
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## Citation |
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To cite this model, please use: |
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``` |
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@online{MerlynEducationModels, |
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author = {Merlyn Mind AI Team}, |
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title = {Merlyn Mind's education-domain language models}, |
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year = {2023}, |
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url = {merlyn.org}, |
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urldate = {2023-06-26} |
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} |
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``` |
