Instructions to use minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e: direct link, hf CLI and curl.
- Browser
- Download file 37.1 MB
-
https://huggingface.co/minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/resolve/main/adapter_model.bin
37.1 MB
- Xet hash:
- 8b6d9b66cc620e79f51870c895188e49bcb3bbe9cf1762cf8635ad70d5eeebc1
- Size of remote file:
- 37.1 MB
- SHA256:
- ce2d9ea3cd001785f0ffcfb02ea8ea0f1e98fec80c9e6ccbf660548e391b7517
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