Instructions to use aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/resolve/main/last-checkpoint/optimizer.pt
671 MB
- Xet hash:
- 890135a36a128355537150d87a33aed27204eef654c8199abba9d0ac83ba24b3
- Size of remote file:
- 671 MB
- SHA256:
- 8365429d8800b858f62fc290701a3871bfb1d27d32ef47c9987bc17981898fe1
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