Instructions to use bulkbeings/emma_assistant_adapters_legacy_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bulkbeings/emma_assistant_adapters_legacy_v0.1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "bulkbeings/emma_assistant_adapters_legacy_v0.1") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bulkbeings/emma_assistant_adapters_legacy_v0.1: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/bulkbeings/emma_assistant_adapters_legacy_v0.1/resolve/main/training_args.bin
- Command line
-
hf download hf://bulkbeings/emma_assistant_adapters_legacy_v0.1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bulkbeings/emma_assistant_adapters_legacy_v0.1/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- b77e86dcda76bd411f82aa070bcb661cff002da12eec79d551ae606bcc52873d
- Size of remote file:
- 5.11 kB
- SHA256:
- 2dcffc82536eae9ec34475d8522da09571db612284fd1397fbda52a0e8879065
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.