Update README.md

README.md

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 ---
 license: apache-2.0
+tags:
+- MerlynMind
+- education
 ---
+
+# Merlyn-education-teacher-assistant
+
+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.
+
+This model was trained by [Merlyn Mind](https://www.merlyn.org/).
+
+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
+
+Apache-2.0
+
+## Documentation
+
+* [Merlyn Mind’s education-specific language models](https://www.merlyn.org/)
+
+## Usage
+
+Loading model and tokenizer:
+
+```python
+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:
+
+```python
+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:"
+```
+
+Inference:
+
+```python
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+generate_ids = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.0,
+    num_beams=2
+)
+response = tokenizer.decode(generate_ids[0],
+                      skip_special_tokens=True,
+                      clean_up_tokenization_spaces=True)
+```
+
+Example output (after response processing):
+
+```json
+[
+{"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."}
+]
+```
+
+## Citation
+
+To cite this model, please use:
+
+```
+@online{MerlynEducationModels,
+    author    = {Merlyn Mind AI Team},
+    title     = {Merlyn Mind's education-domain language models},
+    year      = {2023},
+    url       = {merlyn.org},
+    urldate   = {2023-06-26}
+}
+```