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natolambert/gpt2-dummy-rm
gpt2-dummy-rm is a text classification model from natolambert. Use it when you need a label for a piece of text. It is set up for transformers.
This model is just GPT2 base (~100M param) with a value head appended, untrained. Use this for debugging RLHF setups (could make a smaller one too). The predictions should be somewhat random.
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From the Hugging Face model README
This model is just GPT2 base (~100M param) with a value head appended, untrained. Use this for debugging RLHF setups (could make a smaller one too). The predictions should be somewhat random.
Load the model as follows:
from transformers import AutoModelForSequenceClassification
rm = AutoModelForSequenceClassification.from_pretrained("natolambert/gpt2-dummy-rm")
or as a pipeline
from Transformers import pipeline
reward_pipe = pipeline(
"text-classification",
model="natolambert/gpt2-dummy-rm",
# revision=args.model_revision,
# model_kwargs={"load_in_8bit": True, "device_map": {"": current_device}, "torch_dtype": torch.float16},
)
reward_pipeline_kwargs = {}
pipe_outputs = reward_pipe(texts, **reward_pipeline_kwargs)