Instructions to use apriasmoro/5401ffa9-6e3d-478c-aac2-e3aff6d7efc7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use apriasmoro/5401ffa9-6e3d-478c-aac2-e3aff6d7efc7 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, "apriasmoro/5401ffa9-6e3d-478c-aac2-e3aff6d7efc7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9eeb5d4c27dd2fabb335f72ac0af9adc419c07f2d3a72d2631294611dffbaed
- Size of remote file:
- 7.22 kB
- SHA256:
- 106a9e00a8c62477d26737bc069b256fadad582d7b89d2a4048a13d24f3f378a
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