Instructions to use dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e: direct link, hf CLI and curl.
- Browser
- Download file 35.2 MB
-
https://huggingface.co/dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/dimasik1987/7edb2adf-1ded-408b-9df4-36991c64fb3e/resolve/main/adapter_model.safetensors
35.2 MB
- Xet hash:
- 2fe4a77b3275cd981fec83e73d00eec93ee02f9823b484aebeb1642a43f5a71f
- Size of remote file:
- 35.2 MB
- SHA256:
- f29f9c89379648b8dd161e983b9f4d03f13571a643ff69ce4b64ec915fa090c7
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