Instructions to use reciprocate/gpt-j_rm_format-oa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reciprocate/gpt-j_rm_format-oa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="reciprocate/gpt-j_rm_format-oa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("reciprocate/gpt-j_rm_format-oa") model = AutoModelForSequenceClassification.from_pretrained("reciprocate/gpt-j_rm_format-oa") - Notebooks
- Google Colab
- Kaggle
GPT-J for preference modeling
- Dataset: https://huggingface.co/datasets/reciprocate/oasst_hh_shp_hellaswag_webgpt_rm_dataset
- Logs: https://wandb.ai/sorry/autocrit/runs/y9des5kz?workspace=user-sorry
Usage:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("reciprocate/gpt-j_rm_format-oa", revision="501f895")
model = AutoModelForSequenceClassification.from_pretrained("reciprocate/gpt-j_rm_format-oa", revision="501f895")
input = "<|prompter|>Are you lying right now?</s><|assistant|>I am :)</s><|endoftext|>"
model(**tokenizer(input, return_tensors="pt"))[0].item()
Output:
-4.877448558807373
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