Instructions to use Matas5/gemma-weather-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Matas5/gemma-weather-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-4b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Matas5/gemma-weather-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Matas5/gemma-weather-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 Matas5/gemma-weather-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 Matas5/gemma-weather-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Matas5/gemma-weather-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Matas5/gemma-weather-lora", max_seq_length=2048, )
- Xet hash:
- 2054b02c75256eaf77694ffd663e3b45f940149a17e4aa85c633bf9ec9085b57
- Size of remote file:
- 6.35 kB
- SHA256:
- a3189ef74b874170e5216bec5a993fa67db7381c433381e3e1bb81bf656ccee1
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