Instructions to use dada22231/0f6c5e5c-8b13-466e-a1dd-fef3ee64c44c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/0f6c5e5c-8b13-466e-a1dd-fef3ee64c44c 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, "dada22231/0f6c5e5c-8b13-466e-a1dd-fef3ee64c44c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6082dbcb59e89c4ab5a969d6b69e506670291acee62fbb379878d5bd5e61c20
- Size of remote file:
- 6.84 kB
- SHA256:
- 78e0be1c6acaed5b07b17184951ec7c6011b762d0e245a9312f53908a3a44557
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