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--- |
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library_name: transformers |
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base_model: JuIm/ProGemma2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ProGemma2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ProteinLM |
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This is a custom configuration (336M parameters) of Google’s Gemma 2 LLM that is being pre-trained on amino acid sequences of 512 AA or less in length. Periodic updates are made to this page as training reaches new checkpoints. |
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The purpose of this model was to investigate the differences between ProGemma and ProtGPT (GPT-2 architecture) as it pertains to sequence generation. Training loss is ~1.6. Perplexity scores as well as AlphaFold 3’s ptm, pLDDT, and iptm scores are generally in line with ProtGPT’s scores for sequence lengths < 250, although the testing phase is still very early. I have yet to do testing for sequence lengths > 250. More robust testing is also required for lengths < 250 AA. In my very preliminary testing, HHblit e-values of ~0.1 are achieved without much guidance. |
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Controlled generation is not a capability of this model, and therefore serves as a method to significantly improve generation as, in principal, a sequence that performs a given function or resides in a particular cellular location can be generated. |
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In sequence generation, a top_k of 950 appears to work well as it prevents repetition. This is also seen in ProtGPT. |
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Below is code using the Transformers library to generate sequences using ProGemma. |
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained("JuIm/ProGemma") |
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tokenizer = AutoTokenizer.from_pretrained("JuIm/Amino-Acid-Sequence-Tokenizer") |
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progemma = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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sequence = progemma("\<bos>", top_k=950, max_length=100, num_return_sequences=1, do_sample=True, repetition_penalty=1.2, eos_token_id=21, pad_token_id=22, bos_token_id=20) |
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s = sequence[0]['generated_text'] |
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print(s) |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Tokenizers 0.19.1 |
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