Instructions to use fats-fme/8621c6f3-fff3-4f7c-95a1-d9d5230042d5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/8621c6f3-fff3-4f7c-95a1-d9d5230042d5 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, "fats-fme/8621c6f3-fff3-4f7c-95a1-d9d5230042d5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1dc9c1ccbffd2956de11be0c20b4ae215dc71a8c46d13cec1712c3cbbe1c95d
- Size of remote file:
- 168 MB
- SHA256:
- 6656dc8429316e09ce9fa5126927358f8ceb1190c77cc015ad97adf5fd038249
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