Instructions to use yamatazen/LorablatedStock-12B-LoRA-Rank128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yamatazen/LorablatedStock-12B-LoRA-Rank128 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "yamatazen/LorablatedStock-12B-LoRA-Rank128") - Notebooks
- Google Colab
- Kaggle
LorablatedStock-12B-LoRA-Rank128
This is a LoRA extracted from a language model. It was extracted using mergekit.
LoRA Details
This LoRA adapter was extracted from yamatazen/LorablatedStock-12B and uses unsloth/Mistral-Nemo-Instruct-2407 as a base.
Parameters
The following command was used to extract this LoRA adapter:
C:\Users\yamat\AppData\Local\Programs\Python\Python312\Scripts\mergekit-extract-lora --model yamatazen/LorablatedStock-12B --base-model unsloth/Mistral-Nemo-Instruct-2407 --out-path LorablatedStock-12B-LoRA-Rank128 --max-rank 128
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Base model
unsloth/Mistral-Nemo-Instruct-2407