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README
License: apache-2.0Base model
- google/txgemma-9b-predict
Task
- Input: biochemical reaction SMILES
- Output: EC number (up to sub-subclass level)
Training
- Parameter-efficient fine-tuning using LoRA
- Few-shot prompt format
Usage
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_model = "google/txgemma-9b-predict"adapter_repo = "PlanesLab/Gemma4EC-9B-Predict"tokenizer = AutoTokenizer.from_pretrained(base_model)model = AutoModelForCausalLM.from_pretrained(base_model,torch_dtype="auto",device_map="auto")model = PeftModel.from_pretrained(model, adapter_repo)model.eval()
Code
Full source code including training, inference and benchmarking scripts are available on:
https://github.com/PlanesLab/Gemma4EC
Citation
Model provider
PlanesLab
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Base
google/txgemma-9b-predict
Adapter
this model
Modalities
Input
Text
Output
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Pricing
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