Instructions to use bunnycore/Gemma2-2b-Smart-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunnycore/Gemma2-2b-Smart-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bunnycore/Gemma2-2b-Smart-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use bunnycore/Gemma2-2b-Smart-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 bunnycore/Gemma2-2b-Smart-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 bunnycore/Gemma2-2b-Smart-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bunnycore/Gemma2-2b-Smart-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="bunnycore/Gemma2-2b-Smart-lora", max_seq_length=2048, )
Uploaded model
dataset = load_dataset("TokenBender/roleplay_alpaca", split="train")
dataset = dataset.map(formatting_prompts_func_alpaca, batched=True)
# Load and map the second dataset
dataset2 = load_dataset("Magpie-Align/Magpie-Reasoning-V1-150K-CoT-QwQ", split="train")
dataset2 = dataset2.map(formatting_prompts_func_magpie, batched=True)
- Developed by: bunnycore
- License: apache-2.0
- Finetuned from model : unsloth/gemma-2-2b-it-bnb-4bit
This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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