Instructions to use nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama") model = PeftModel.from_pretrained(base_model, "nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56a40a544e67d1d14d77d20471f4b8ee2ed2e26609edc531035ef7440fa534b7
- Size of remote file:
- 6.78 kB
- SHA256:
- 63c8afb2fbdc05a2422f8730146729a1bf7f68356f35ddb02e68b5ddcd83c28f
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