Instructions to use melsiddieg/maktba_lora_model-200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melsiddieg/maktba_lora_model-200 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("melsiddieg/maktba_lora_model-200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from melsiddieg/maktba_lora_model-200: direct link, hf CLI and curl.
- Browser
- Download file 11.5 MB
-
https://huggingface.co/melsiddieg/maktba_lora_model-200/resolve/main/tokenizer.json
- Command line
-
hf download hf://melsiddieg/maktba_lora_model-200/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/melsiddieg/maktba_lora_model-200/resolve/main/tokenizer.json
11.5 MB
- Xet hash:
- 5e12cb7c0d1e452206f5eb40c567375b27c06025c1e3e60797c004c34077d3d7
- Size of remote file:
- 11.5 MB
- SHA256:
- 4afd2909b31c956de44ca80313a4d5bb19a5f51c208a2f685062043671baa1b0
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