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license: bsd-3-clause |
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Mirror of the base ProGen2-medium model (with slightly modified configuration and forward pass) introduced by [Nijkamp, et al.](https://arxiv.org/abs/2206.13517). |
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See also my github [repo](https://github.com/hugohrban/ProGen2-finetuning/tree/main) for an example of finetuning this model. |
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Example usage: |
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```python |
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from transformers import AutoModelForCausalLM |
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from tokenizers import Tokenizer |
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import torch |
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import torch.nn.functional as F |
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# load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-medium", trust_remote_code=True) |
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tokenizer = Tokenizer.from_pretrained("hugohrban/progen2-medium") |
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tokenizer.no_padding() |
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# prepare input |
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prompt = "1MEVVIVTGMSGAGK" |
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input_ids = torch.tensor(tokenizer.encode(prompt).ids).to(model.device) |
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# forward pass |
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logits = model(input_ids).logits |
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# print output probabilities |
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next_token_logits = logits[-1, :] |
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next_token_probs = F.softmax(next_token_logits, dim=-1) |
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for i in range(tokenizer.get_vocab_size(with_added_tokens=False)): |
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print(f"{tokenizer.id_to_token(i)}: {100 * next_token_probs[i].item():.2f} %") |
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``` |
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