Instructions to use eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/rng_state_2.pth from eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279: direct link, hf CLI and curl.
- Browser
- Download file 15 kB
-
https://huggingface.co/eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/resolve/main/last-checkpoint/rng_state_2.pth
- Command line
-
hf download hf://eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/last-checkpoint/rng_state_2.pth
-
curl -L -o rng_state_2.pth https://huggingface.co/eeeebbb2/f30106c8-08a4-41b0-a05c-39ceb3d03279/resolve/main/last-checkpoint/rng_state_2.pth
15 kB
- Xet hash:
- 546899def21dfb3ad0fc0d7ac1bd43c35919ad972415e78f3fe872429c8eb3e9
- Size of remote file:
- 15 kB
- SHA256:
- bce694881769a07bcc081ca97dec751ab687b9ff56322c475a0822d5698c6d3c
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