Instructions to use dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-Storm-8B") model = PeftModel.from_pretrained(base_model, "dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- d655a152b3d7036312fb5f93109939c3e9a9f3aca3d097a86df040bb85d9045a
- Size of remote file:
- 6.78 kB
- SHA256:
- 63dde5d3bef68848519e3a5b18e6eea971e14ae7359fd833dd4afe1683839bd8
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