# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("reciprocate/openllama-13b_rm_oasst-hh")
model = AutoModelForSequenceClassification.from_pretrained("reciprocate/openllama-13b_rm_oasst-hh", device_map="auto")Quick Links
OpenLLama-13B for reward modeling
- Dataset: https://huggingface.co/datasets/pvduy/rm_oa_hh
- Logs: https://wandb.ai/sorry/autocrit/runs/j05t4e97?workspace=user-sorry
- Code: https://github.com/CarperAI/autocrit/blob/main/train_reward_model.py
Usage:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
ckpt = "reciprocate/openllama-13b_rm_oasst-hh"
model = AutoModelForSequenceClassification.from_pretrained(ckpt, load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model(**tokenizer("ASSISTANT: This sentence is a lie.", return_tensors="pt"))[0].item()
Output:
-1.626953125
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="reciprocate/openllama-13b_rm_oasst-hh")