Instructions to use samarthagarwalgit/Jarvis-GrokBot-Gemma4-E2B-Iter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samarthagarwalgit/Jarvis-GrokBot-Gemma4-E2B-Iter2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("samarthagarwalgit/Jarvis-GrokBot-Gemma4-E2B-Iter2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Uploaded finetuned model
- Developed by: samarthagarwalgit
- License: apache-2.0
- Finetuned from model : unsloth/gemma-4-E2B-it-unsloth-bnb-4bit
This gemma4 model was trained 2x faster with Unsloth
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Model tree for samarthagarwalgit/Jarvis-GrokBot-Gemma4-E2B-Iter2
Base model
google/gemma-4-E2B Finetuned
google/gemma-4-E2B-it Quantized
unsloth/gemma-4-E2B-it-unsloth-bnb-4bit