# Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("baseten/gemma-4-e2b-it-sequence-classification", trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("baseten/gemma-4-e2b-it-sequence-classification", trust_remote_code=True)Quick Links
Gemma 4 E2B IT Sequence Classification
This checkpoint converts google/gemma-4-E2B-it into a two-label sequence
classifier by slicing the original LM head to the single-token labels no and
yes.
The classifier logits are exactly the original next-token logits for:
no: token id1904yes: token id4443
Load with custom code enabled:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_id = "baseten/gemma-4-e2b-it-sequence-classification"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
trust_remote_code=True,
dtype="auto",
)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baseten/gemma-4-e2b-it-sequence-classification", trust_remote_code=True)