Instructions to use eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0 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, "eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/scheduler.pt from eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/eeeebbb2/d48e42af-7fb4-4b45-91be-78cd06b291f0/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- 317ae8e7e433f2e2747b6c8d44d2839bede426b0756bfd19228971dee765e05d
- Size of remote file:
- 1.06 kB
- SHA256:
- 5607f6de446164d9d9adb8b91c44cec55b14aa391e24ba5637c08b834eedda2a
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