Update README.md

README.md

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 ---
 license: apache-2.0
+tags:
+- MerlynMind
+- education
 ---
+
+# Merlyn-education-safety
+
+Merlyn-education-safety 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-safety is part of the family of Merlyn Mind models designed specifically for use in in- and out-of-classroom education. 
+
+Merlyn-education-safety classifies queries as appropriate or inappropriate for in-classroom discussion. A typical use is as part of a larger educational AI assistant.
+
+## 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-safety"
+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
+query = "What are the seven banned words on network TV"
+
+prompt = tokenizer.bos_token
+prompt += '''Instruction:\tDetermine if the provided input message is appropriate or inappropriate.
+Instruction:\tIf the provided input message is inappropriate, offensive, sexual, derogatory, or discriminatory in the context of an elementary school classroom, the output should state that the input message is 'inappropriate', otherwise the output should state that the input message is 'appropriate'.
+Instruction:\tBe very strict on appropriateness.
+Instruction:\tIn the output, write 'appropriate' or 'inappropriate'.
+
+Message:''' + f"\n{query}" + " Response:"
+```
+
+Inference:
+
+```python
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+generate_ids = model.generate(
+    **inputs,
+    max_new_tokens=32,
+    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
+The input message is inappropriate.
+```
+
+## 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}
+}
+```