Instructions to use allenai/Llama-3.1-70B-Instruct-RM-RB2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/Llama-3.1-70B-Instruct-RM-RB2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="allenai/Llama-3.1-70B-Instruct-RM-RB2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("allenai/Llama-3.1-70B-Instruct-RM-RB2") model = AutoModelForSequenceClassification.from_pretrained("allenai/Llama-3.1-70B-Instruct-RM-RB2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model-00013-of-00029.bin from allenai/Llama-3.1-70B-Instruct-RM-RB2: direct link, hf CLI and curl.
- Browser
- Download file 5 GB
-
https://huggingface.co/allenai/Llama-3.1-70B-Instruct-RM-RB2/resolve/664f2a01f0dd0d65072f4750c7387c45dd4c64bf/pytorch_model-00013-of-00029.bin
- Command line
-
hf download hf://allenai/Llama-3.1-70B-Instruct-RM-RB2@664f2a01f0dd0d65072f4750c7387c45dd4c64bf/pytorch_model-00013-of-00029.bin
-
curl -L -o pytorch_model-00013-of-00029.bin https://huggingface.co/allenai/Llama-3.1-70B-Instruct-RM-RB2/resolve/664f2a01f0dd0d65072f4750c7387c45dd4c64bf/pytorch_model-00013-of-00029.bin
5 GB
- Xet hash:
- 5bac2fef4a992d928fc52784c98a7098744ee46eb8602b08b42b5ed3e8be2b27
- Size of remote file:
- 5 GB
- SHA256:
- 8b1df2e0a0a8ce8d708758005bec905a78499fc240a7ddce1b58d00958b62ae6
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