Instructions to use eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8 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, "eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/rng_state_2.pth from eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8: direct link, hf CLI and curl.
- Browser
- Download file 15 kB
-
https://huggingface.co/eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8/resolve/main/last-checkpoint/rng_state_2.pth
- Command line
-
hf download hf://eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8/last-checkpoint/rng_state_2.pth
-
curl -L -o rng_state_2.pth https://huggingface.co/eeeebbb2/57473b92-4192-49cb-9c74-0b29a21addf8/resolve/main/last-checkpoint/rng_state_2.pth
15 kB
- Xet hash:
- 4ce2f5c7681d1346280e4ab1f4df672242b810e067fa4632b0aa774574b961a4
- Size of remote file:
- 15 kB
- SHA256:
- dd2be4f2486a52117e9895d2cb9366afb243f8b57fbf8003f080572c9e878465
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