Instructions to use ishanarang/lora_model_it_updated_longdata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ishanarang/lora_model_it_updated_longdata with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ishanarang/lora_model_it_updated_longdata", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use ishanarang/lora_model_it_updated_longdata 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 ishanarang/lora_model_it_updated_longdata 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 ishanarang/lora_model_it_updated_longdata to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ishanarang/lora_model_it_updated_longdata to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ishanarang/lora_model_it_updated_longdata", max_seq_length=2048, )
- Xet hash:
- 2b85827a84157d439cdbd25758afce1e6f7c75e0564643ce4753b3f7b05a267f
- Size of remote file:
- 17.2 MB
- SHA256:
- 90883524dbec2e8c465564ac46b4e5298235668a5cf8523690f06f45f51646fe
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