Instructions to use robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/tokenizer.model from robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7/resolve/main/last-checkpoint/tokenizer.model
- Command line
-
hf download hf://robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7/last-checkpoint/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/robiulawaldev/9c58422c-4b66-4fca-8308-373ce5071dd7/resolve/main/last-checkpoint/tokenizer.model
4.24 MB
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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