Update README.md

README.md

@@ -5,7 +5,27 @@ license: bsd-3-clause
 # ProCALM
 [ProCALM](https://github.com/jsunn-y/ProCALM/tree/main) (Protein Conditionally Adapted Language Model) is a suite of models where [ProGen2-base](https://github.com/enijkamp/progen2) is finetuned with conditional adapters for conditional generation of functional enzymes, based on EC number, taxonomy, or both.
 
-ProCALM models share `tokenizer.json` and individual models are organized into subfolders. We have uploaded the most relevant models here, but please reach out if you would like to use other models from our paper. `1.5B` and `9B` refer to checkpoints trained to 1.5 and 9 billion tokens, respectively. More usage details can be found in [github](https://github.com/jsunn-y/ProCALM/tree/main) and in our paper.
+ProCALM models share `tokenizer.json` and individual models are organized into subfolders. We have uploaded the most relevant models here, but please reach out if you would like to use other models from our paper. `1.5B` and `9B` refer to checkpoints trained to 1.5 and 9 billion tokens, respectively. 
+
+## Quickstart
+Usage details with examples can be found in [github](https://github.com/jsunn-y/ProCALM/tree/main) under "Generation" and in our paper. Example framework for generation from pretrained models:
+```
+from tokenizers import Tokenizer
+from model import ProgenConditional
+
+model = ProgenConditional.from_pretrained("jsunn-y/ProCALM", subfolder="ec-onehot-swissprot/1.5B")
+tokenizer = Tokenizer.from_pretrained("jsunn-y/ProCALM")
+
+with torch.no_grad():
+  input_ids = torch.tensor(self.tokenizer.encode(context).ids).view([1, -1]).to(self.device)
+  tokens_batch = model.generate(input_ids=input_ids, condition_encodings=condition_encodings, do_sample=True, temperature=temperature, max_length=max_length, top_p=top_p, num_return_sequences=num_return_sequences, pad_token_id=self.pad_token_id, eos_token_id=4)
+
+as_lists = lambda batch: [batch[i, ...].detach().cpu().numpy().tolist() for i in range(batch.shape[0])]
+sequences = tokenizer.decode_batch(as_lists(tokens_batch))
+```
+Note that condition_encodings is a representation of the conditioning, which can be calculated using the dictionaries `.pt` provided in our github under `data`.
+
+## Summary of Available Models
 
 | Name | Description |
 |:--------|:-------:|