Instructions to use dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dada22231/75e85b72-6648-49d6-9f3e-75d68ddfd43e/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 74097aa4ab42bfbd3ebb5747a24327b9b04861f06e2c4bba562c2634373dea5a
- Size of remote file:
- 336 MB
- SHA256:
- 2e727bc95f8acd79cb50014f3e32e8c3a253eeafc3dc63bb72289d7b8bdead0f
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