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 training_args.bin from minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/0798698e-3d5a-4d52-bbc5-4828b63b6a5e/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 8e5d97d3d852f09fb00dbaa2ebec7dd36ffdc42f83ba210dc29a6ecade050b0d
- Size of remote file:
- 6.78 kB
- SHA256:
- 8cff38cb40b0009c66b55f086275e5eeaf1e38f9d7ab619ba6212f9f6aa2a976
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