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mrothroc
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mrothroc
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GPT2LMHeadModel.Scored on 30,000 unconditional samples with the official moses.metrics:
| Metric | this model | MolGPT (published) |
|---|---|---|
| Valid | 0.988 | 0.994 |
| Unique@10k | 0.991 | ~1.000 |
| Novelty | 0.783 | 0.797 |
| IntDiv | 0.857 | 0.857 |
Within a point on every firm metric. FCD/Test is 2.83; MolGPT never published an FCD at this size, so there is no firm number to match (the SELFIES sibling below reaches 0.43).
It loads with stock transformers as a GPT2LMHeadModel, no trust_remote_code. The framing tokens are BOS=1, EOS=2, PAD=0: prompt with BOS, sample until EOS, and read the SMILES off the tokens.
python
import torchfrom transformers import AutoModelForCausalLMfrom tokenizers import Tokenizerfrom huggingface_hub import hf_hub_downloadrepo = "mrothroc/molgpt-moses-smiles-mixlab"model = AutoModelForCausalLM.from_pretrained(repo).eval() # GPT2LMHeadModeltok = Tokenizer.from_file(hf_hub_download(repo, "tokenizer.json"))BOS, EOS, PAD = 1, 2, 0out = model.generate(torch.full((8, 1), BOS), do_sample=True, temperature=1.0,top_k=0, max_length=65, eos_token_id=EOS, pad_token_id=PAD)for row in out.tolist():seq = [t for t in row[1:] if t not in (BOS, PAD)]if EOS in seq: seq = seq[:seq.index(EOS)]print("".join(tok.id_to_token(t) for t in seq)) # SMILES strings
mrothroc/molgpt-moses-selfies-mixlab retrains the same model on SELFIES, a representation where every string decodes to a valid molecule by construction: validity 1.000, Novelty 0.859, FCD/Test 0.43. Same size, same budget, better numbers, from changing the representation rather than the model.
The full recipe (reproduce, three ways to guarantee validity, and goal-directed fine-tuning) is in the mixlab cookbook: https://github.com/mrothroc/mixlab-cookbook. Trainer: mixlab.
For the story behind it, read You Don't Code the Model, You Edit the JSON.
This is a reproduction, so please cite what it builds on: