Instructions to use abaddon182/0c3069ec-7f96-4c89-9b60-975a4be1decc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abaddon182/0c3069ec-7f96-4c89-9b60-975a4be1decc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "abaddon182/0c3069ec-7f96-4c89-9b60-975a4be1decc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b02ea79111014de3dccbf46b20cefc2dbbe481fb6707c90b2b748d545a2b7fc0
- Size of remote file:
- 332 MB
- SHA256:
- 0290ab3455e88dd1d1d2c0863efc83fe2ee84aec8ca4eaf192e67a3925c053ac
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