# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("lancehar/last_linear_seed5", trust_remote_code=True, device_map="auto")Quick Links
lancehar/last_linear_seed5
This repository contains the last_linear_seed5 head-diverse reward model checkpoint from
mc-dropout-vs-reward-ensemble.
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "lancehar/last_linear_seed5"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
trust_remote_code=True,
)
These checkpoints use a custom GPT-NeoX reward-model class, so trust_remote_code=True
is required unless you register the class locally from this repository.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lancehar/last_linear_seed5", trust_remote_code=True)