Instructions to use Siddartha10/outputs_gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Siddartha10/outputs_gemma with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Siddartha10/outputs_gemma") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from Siddartha10/outputs_gemma: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/Siddartha10/outputs_gemma/resolve/main/tokenizer.json
- Command line
-
hf download hf://Siddartha10/outputs_gemma/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Siddartha10/outputs_gemma/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- 468f89786a084f976ebe64a0fb2ecf73029025701e08d5a125aa4239fff2908e
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
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