Instructions to use dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/rng_state_2.pth from dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5: direct link, hf CLI and curl.
- Browser
- Download file 15 kB
-
https://huggingface.co/dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/resolve/main/last-checkpoint/rng_state_2.pth
- Command line
-
hf download hf://dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/last-checkpoint/rng_state_2.pth
-
curl -L -o rng_state_2.pth https://huggingface.co/dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/resolve/main/last-checkpoint/rng_state_2.pth
15 kB
- Xet hash:
- f56ac30869ba04eb013e03fd6b6f6870a8f2ec568fbe2e963c28c2673749e8b6
- Size of remote file:
- 15 kB
- SHA256:
- 121063f6ec13b8e25517fdfbc7084dc05997cc90a700bb89fc412be2a7ce451d
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