Instructions to use shibajustfor/a620d886-b805-48bd-8615-2a268f332be8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a620d886-b805-48bd-8615-2a268f332be8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/CodeLlama-7b-hf-flash") model = PeftModel.from_pretrained(base_model, "shibajustfor/a620d886-b805-48bd-8615-2a268f332be8") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/scheduler.pt from shibajustfor/a620d886-b805-48bd-8615-2a268f332be8: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- 8ffd8647e6836f657305bfb3dce6254aecc45c725a5af009395c38f71c05735e
- Size of remote file:
- 1.06 kB
- SHA256:
- 9b80fcc7599efca0c6313d990c467c2eb3001742b23ddaadc22e3499c12cea79
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