Instructions to use aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("jhflow/mistral7b-lora-multi-turn-v2") model = PeftModel.from_pretrained(base_model, "aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/scheduler.pt from aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- 26a8add36d5219d9b626f439e0bd726310d4f1388ad117a1d37e5a656eae6779
- Size of remote file:
- 1.06 kB
- SHA256:
- 891cad020bf7bee78efa739dc10e1e4315e34b096ed70226b38590ec81d7d418
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