Instructions to use minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.5") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be: direct link, hf CLI and curl.
- Browser
- Download file 125 MB
-
https://huggingface.co/minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/c3615ffb-8e90-4556-8b24-1f552e2e49be/resolve/main/adapter_model.bin
125 MB
- Xet hash:
- a0700fad81e60473ad20729b6ed087fc6ae11b6427d182802df2eece2f024778
- Size of remote file:
- 125 MB
- SHA256:
- 9db7e5582b12068a2c43a8b0f1fadb2f84c5df4eb728ae405136fb07fa0de172
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