Instructions to use mchl-labs/stambecco-13b-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mchl-labs/stambecco-13b-plus with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("decapoda-research/llama-13b-hf") model = PeftModel.from_pretrained(base_model, "mchl-labs/stambecco-13b-plus") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from mchl-labs/stambecco-13b-plus: direct link, hf CLI and curl.
- Browser
- Download file 26.3 MB
-
https://huggingface.co/mchl-labs/stambecco-13b-plus/resolve/main/adapter_model.bin
- Command line
-
hf download hf://mchl-labs/stambecco-13b-plus/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/mchl-labs/stambecco-13b-plus/resolve/main/adapter_model.bin
26.3 MB
- Xet hash:
- 182041220930283c79158b9ba1746374f99ff2091a92bd7170d91d28e7fe8aaa
- Size of remote file:
- 26.3 MB
- SHA256:
- 22b351b435e6a650b5a883ee17cb3f26d818a98c8d42f8afb0cc0902774ebca1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.