Instructions to use mgbam/gemma-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgbam/gemma-3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mgbam/gemma-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from mgbam/gemma-3: direct link, hf CLI and curl.
- Browser
- Download file 602 Bytes
-
https://huggingface.co/mgbam/gemma-3/resolve/main/README.md
- Command line
-
hf download hf://mgbam/gemma-3/README.md
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curl -L -o README.md https://huggingface.co/mgbam/gemma-3/resolve/main/README.md
602 Bytes
metadata
base_model: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: mgbam
- License: apache-2.0
- Finetuned from model : unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit
This gemma3n model was trained 2x faster with Unsloth and Huggingface's TRL library.
