Instructions to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/daae6fca-7305-4b21-9b88-210da89e28b3") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from cimol/daae6fca-7305-4b21-9b88-210da89e28b3: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/main/tokenizer.json
- Command line
-
hf download hf://cimol/daae6fca-7305-4b21-9b88-210da89e28b3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 6754daf9fac4206f7b5fb393033c99521f170ed1da810e3fa61519aa3eb1a204
- Size of remote file:
- 17.2 MB
- SHA256:
- ade1dac458f86f9bea8bf35b713f14e1bbed24228429534038e9f7e54ea3e8b6
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