Instructions to use roshanrb001/lora_model_gemma3_un with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roshanrb001/lora_model_gemma3_un with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("roshanrb001/lora_model_gemma3_un", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from roshanrb001/lora_model_gemma3_un: direct link, hf CLI and curl.
- Browser
- Download file 586 Bytes
-
https://huggingface.co/roshanrb001/lora_model_gemma3_un/resolve/main/README.md
- Command line
-
hf download hf://roshanrb001/lora_model_gemma3_un/README.md
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curl -L -o README.md https://huggingface.co/roshanrb001/lora_model_gemma3_un/resolve/main/README.md
586 Bytes
metadata
base_model: unsloth/gemma-3-4b-it-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: roshanrb001
- License: apache-2.0
- Finetuned from model : unsloth/gemma-3-4b-it-bnb-4bit
This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
