Instructions to use eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("JackFram/llama-68m") model = PeftModel.from_pretrained(base_model, "eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eeeebbb2/8cee0fd2-9484-42cb-a713-0b4c64bc3001/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 81ba04bc0e952501a1c842edb554a1b080416b2db03a36c5202c3844395c6243
- Size of remote file:
- 6.84 kB
- SHA256:
- 2f8ea6489d59372128261dd3d440bec8d1e24d572c1451cb2367293d6d255b1a
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