| --- |
| 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](https://huggingface.co/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](https://github.com/PlanesLab/Gemma4EC) |
|
|
| ## Usage |
|
|
| The base model is gated: request access to [google/txgemma-2b-predict](https://huggingface.co/google/txgemma-2b-predict), then `huggingface-cli login`. |
|
|
| ```python |
| 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](https://github.com/rxn4chemistry/biocatalysis-model). |
|
|
| ## Citation |
|
|
| Please cite Gemma4EC (see the [repository](https://github.com/PlanesLab/Gemma4EC)) and |
| [TxGemma](https://huggingface.co/google/txgemma-2b-predict). |
|
|
| ## License |
|
|
| Adapter weights: Apache-2.0. TxGemma weights: [Health AI Developer Foundations terms](https://developers.google.com/health-ai-developer-foundations/terms). |
|
|
