Instructions to use ankit-pn/gemma4-e2b-calculus-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ankit-pn/gemma4-e2b-calculus-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-e2b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "ankit-pn/gemma4-e2b-calculus-lora") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ankit-pn/gemma4-e2b-calculus-lora: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/ankit-pn/gemma4-e2b-calculus-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://ankit-pn/gemma4-e2b-calculus-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ankit-pn/gemma4-e2b-calculus-lora/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.