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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from reciprocate/gpt-j_rm_format-oa: direct link, hf CLI and curl.
- Browser
- Download file 11.8 GB
-
https://huggingface.co/reciprocate/gpt-j_rm_format-oa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://reciprocate/gpt-j_rm_format-oa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/reciprocate/gpt-j_rm_format-oa/resolve/main/pytorch_model.bin
11.8 GB
- Xet hash:
- 0a7ad244e7e1798b1ddcf7da0e11e4c7ccc7eaf558742194a89483cf142e6cc5
- Size of remote file:
- 11.8 GB
- SHA256:
- 307a5503f7a484f87e5237c5420ef559549d1a7629548d8aea9c22aa583de144
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