metadata
license: apache-2.0
base_model: google/txgemma-2b-predict
library_name: peft
pipeline_tag: text-generation
tags:
- gemma
- txgemma
- ec-number
- enzyme-commission
- biochemical-reactions
- lora
- peft
- gemma4ec
Gemma4EC-2B-Predict
LoRA adapter for google/txgemma-2b-predict that predicts the Enzyme Commission (EC) number of a biochemical reaction from its SMILES, down to the sub-subclass level.
- Input: a reaction SMILES, e.g.
CCO.O=O>>CC=O.OO - Output: an EC number, e.g.
1.01.03 - Code, preprocessing and benchmarks: github.com/PlanesLab/Gemma4EC
Usage
The base model is gated: request access to google/txgemma-2b-predict, then huggingface-cli login.
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import 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:"
)
inputs = tokenizer(PROMPT.format(reaction_smiles="CCO.O=O>>CC=O.OO"), 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())
Use this exact prompt. Preprocess SMILES with preprocess_smiles.py from the repository first; batch prediction
and chat explanations are also there.
Training
LoRA (r = 16, alpha = 16) on q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj,
fine-tuned on ECReact.
Citation
Please cite Gemma4EC (see the repository) and TxGemma.
License
Adapter weights: Apache-2.0. TxGemma weights: Health AI Developer Foundations terms.