Instructions to use shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3: direct link, hf CLI and curl.
- Browser
- Download file 13.7 MB
-
https://huggingface.co/shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/resolve/main/last-checkpoint/optimizer.pt
13.7 MB
- Xet hash:
- 6b20340bb41acf16add6a29181853887bfd1a0c04ddce5f1d5be9adadb58c2f3
- Size of remote file:
- 13.7 MB
- SHA256:
- 65be91c8e2e79b9cbe24ef0dd199d3b20b680763f2b7cf70e7a64ea9809beb89
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