Instructions to use minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/bce67e43-a7e3-46b5-9898-5af8e70fa2c0/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- 2e2ec558736dbdcfba907ac1be93d16b3007271c2f6a66eeb99c1f3814d19629
- Size of remote file:
- 84 MB
- SHA256:
- 91ee8db3ef3f68219874acb16d2794fcd3e3ee22dd9e85afdff289450f0744fe
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