Instructions to use burtenshaw/tmax2-rawcmd-tmaxsft-lora-lr1e5-r8-s80 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use burtenshaw/tmax2-rawcmd-tmaxsft-lora-lr1e5-r8-s80 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("allenai/tmax-2b") model = PeftModel.from_pretrained(base_model, "burtenshaw/tmax2-rawcmd-tmaxsft-lora-lr1e5-r8-s80") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96a7c76346bb1f7b977d7c34515e11728ffa14e5f65105db6912221075c5150d
- Size of remote file:
- 33.7 MB
- SHA256:
- 0430ec14e2cf2d1e7aad686e5e2744df06ed2aae71c2cee2c8cebbd2117c0243
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.