Instructions to use nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head: direct link, hf CLI and curl.
- Browser
- Download file 142 Bytes
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https://huggingface.co/nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head/resolve/main/README.md
- Command line
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hf download hf://nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head/README.md
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curl -L -o README.md https://huggingface.co/nm-testing/Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head/resolve/main/README.md
142 Bytes
metadata
library_name: transformers
tags:
- kv-cache
- fp8
Llama-3.3-70B-Instruct-QKV-Cache-FP8-Per-Head
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