Instructions to use dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("echarlaix/tiny-random-mistral") model = PeftModel.from_pretrained(base_model, "dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc: direct link, hf CLI and curl.
- Browser
- Download file 127 kB
-
https://huggingface.co/dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/ed20e542-9648-402a-845b-f096cbb99cfc/resolve/main/last-checkpoint/optimizer.pt
127 kB
- Xet hash:
- 5207c02712fee332bf702fc5a23d0628cd66f0a46b6e4bfec7aac58044388326
- Size of remote file:
- 127 kB
- SHA256:
- 0c06da1bc4207d56e07328a440f79971d6e0469367c26ea2f6aeb051bfa12d2c
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