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PlanesLab
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Model Details
Model Provider
PlanesLab
Model Tree
google/txgemma-2b-predict
Input Modalities
Output Modalities
Supported Functionality
Benchmarked on 10 deduplicated, EC-stratified re-splits of ECReact (57,464 reaction–EC pairs, 80/10/10 per seed). Support-weighted MCC at the EC sub-subclass level:
| MCC | |
|---|---|
| Gemma4EC-2B, mean ± SD over the 10 seeds | 0.903 ± 0.005 |
| These weights (seed 6) | 0.912 (precision 0.912, recall 0.917) |
Gemma4EC outperforms BEC-Pred (0.879), Theia (0.877), CLAIRE (0.864), SelenzymeRF (0.675), SIMMER (0.610) and few-shot Gemini 3.1 Flash-Lite (0.655) and GPT-4.1-mini (0.234) on this benchmark. Full tables and significance tests are in the GitHub repository.
The base model is gated: request access to google/txgemma-2b-predict once, then huggingface-cli login.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "google/txgemma-2b-predict"tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "PlanesLab/Gemma4EC-2B-Predict").eval() PROMPT = ( "Instructions: Given a reaction SMILES, predict the first three parts of its " "Enzyme Commission (EC) number (class.subclass.sub-subclass).\n" "EC numbers are four-part hierarchical codes (e.g., 1.3.2.4) describing the type " "of enzyme and its reaction mechanism.\n" "Only output the first three digits (e.g., 1.03.02) representing the enzyme's " "general class, subclass, and sub-subclass.\n" "Reaction SMILES: {reaction_smiles}\nAnswer:") prompt = PROMPT.format(reaction_smiles="CCO.O=O>>CC=O.OO")inputs = tokenizer(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=8, do_sample=False)print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip())The prompt above is the one used in training; a different prompt will degrade accuracy. For batch prediction, explanations of the predictions and the full benchmark, use predict.py and chat/explain.sh in the repository.
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj).Please cite the Gemma4EC paper (see the repository) and TxGemma.
The adapter weights are released under Apache-2.0. The base TxGemma weights are subject to the Health AI Developer Foundations terms.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "google/txgemma-2b-predict"tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "PlanesLab/Gemma4EC-2B-Predict").eval() PROMPT = ( "Instructions: Given a reaction SMILES, predict the first three parts of its " "Enzyme Commission (EC) number (class.subclass.sub-subclass).\n" "EC numbers are four-part hierarchical codes (e.g., 1.3.2.4) describing the type " "of enzyme and its reaction mechanism.\n" "Only output the first three digits (e.g., 1.03.02) representing the enzyme's " "general class, subclass, and sub-subclass.\n" "Reaction SMILES: {reaction_smiles}\nAnswer:") prompt = PROMPT.format(reaction_smiles="CCO.O=O>>CC=O.OO")inputs = tokenizer(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=8, do_sample=False)print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip())