Instructions to use azuki-digital/gemma-2-27b-it-2_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use azuki-digital/gemma-2-27b-it-2_lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("azuki-digital/gemma-2-27b-it-2_lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use azuki-digital/gemma-2-27b-it-2_lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for azuki-digital/gemma-2-27b-it-2_lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for azuki-digital/gemma-2-27b-it-2_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for azuki-digital/gemma-2-27b-it-2_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="azuki-digital/gemma-2-27b-it-2_lora", max_seq_length=2048, )
metadata
library_name: transformers
tags:
- unsloth
license: gemma
datasets:
- llm-jp/magpie-sft-v1.0
- DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k
- weblab-GENIAC/Open-Platypus-Japanese-masked
base_model:
- google/gemma-2-27b
学習データ
以下のデータセットを使用。
- llm-jp/magpie-sft-v1.0 (apache-2.0)
- DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k (apache-2.0)
- weblab-GENIAC/Open-Platypus-Japanese-masked (MIT)
- MITライセンスのデータのみ抽出して使用。
gemma-2利用にあたり、ライセンス上制約の懸念のあるデータセットは利用していない。
Loraアダプタのみ保存している。