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---
library_name: peft
datasets:
- InstaDeepAI/nucleotide_transformer_downstream_tasks_revised
metrics:
- f1
base_model:
- tattabio/gLM2_150M
model-index:
- name: alejandralopezsosa/gLM2_150M-promoter_tata-lora
results:
- task:
type: sequence-classification
dataset:
type: InstaDeepAI/nucleotide_transformer_downstream_tasks_revised
name: nucleotide_transformer_downstream_tasks_revised
config: promoter_tata
split: test
revision: c8c94743d3d2838b943398ee676247ac2f774122
metrics:
- type: f1
value: 0.9811
---
# gLM2 LoRA adapter for TATA promoter recognition
This model demonstrates the use of [gLM2_150M](https://huggingface.co/tattabio/gLM2_150M) embeddings for downstream classification.
The model is fine-tuned using LoRA and obtains an F1 score of 98.11% on the TATA promoter task from the [Nucleotide Transformer benchmarks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks_revised).
## How to Get Started with the Model
Use the code below to use the model for inference:
```python
from peft import PeftModel
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoModel
glm2 = "tattabio/gLM2_150M"
adapter = "alejandralopezsosa/gLM2_150M-promoter_tata-lora"
load_kwargs = {
'trust_remote_code': True,
'torch_dtype': torch.bfloat16,
}
config = AutoConfig.from_pretrained(adapter, **load_kwargs)
base_model = AutoModelForSequenceClassification.from_config(config, **load_kwargs)
base_model.glm2 = AutoModel.from_pretrained("tattabio/gLM2_150M", **load_kwargs)
model = PeftModel.from_pretrained(base_model, adapter)
```
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