Instructions to use efficiencyx/Jun-LoRA-Adapter-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficiencyx/Jun-LoRA-Adapter-v6 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficiencyx/Jun-LoRA-Adapter-v6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from efficiencyx/Jun-LoRA-Adapter-v6: direct link, hf CLI and curl.
- Browser
- Download file 597 Bytes
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https://huggingface.co/efficiencyx/Jun-LoRA-Adapter-v6/resolve/main/README.md
- Command line
-
hf download hf://efficiencyx/Jun-LoRA-Adapter-v6/README.md
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curl -L -o README.md https://huggingface.co/efficiencyx/Jun-LoRA-Adapter-v6/resolve/main/README.md
597 Bytes
metadata
base_model: unsloth/gemma-4-12B-it-qat-q4_0-unquantized
tags:
- text-generation-inference
- transformers
- unsloth
- gemma4_unified
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: efficiencyx
- License: apache-2.0
- Finetuned from model : unsloth/gemma-4-12B-it-qat-q4_0-unquantized
This gemma4_unified model was trained 2x faster with Unsloth
