Instructions to use dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a: direct link, hf CLI and curl.
- Browser
- Download file 141 MB
-
https://huggingface.co/dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dada22231/7deb2616-685c-4bbf-8ec9-faa8d781e17a/resolve/main/last-checkpoint/optimizer.pt
141 MB
- Xet hash:
- 009d270d08ab108737b52c7abf06f3be72896ffa1ad17328fd9b5100df798c4a
- Size of remote file:
- 141 MB
- SHA256:
- 9500e1912f37a2999ba3799692e1ad9ca630c23d7448a75c990c8c419fe789ec
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