Instructions to use PythonCreate/lora_model_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PythonCreate/lora_model_trained with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PythonCreate/lora_model_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use PythonCreate/lora_model_trained 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 PythonCreate/lora_model_trained 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 PythonCreate/lora_model_trained to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for PythonCreate/lora_model_trained to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="PythonCreate/lora_model_trained", max_seq_length=2048, )
File size: 134 Bytes
a08be45 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:0e0092bccceed6beb0b64ba2cf85f5fda2a44c818768d6cf5c65d8e89f7987ec
size 167832240
|