Instructions to use socius/Smoltaur-0.4B-LoRA-r64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use socius/Smoltaur-0.4B-LoRA-r64 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M") model = PeftModel.from_pretrained(base_model, "socius/Smoltaur-0.4B-LoRA-r64") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use socius/Smoltaur-0.4B-LoRA-r64 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 socius/Smoltaur-0.4B-LoRA-r64 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 socius/Smoltaur-0.4B-LoRA-r64 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for socius/Smoltaur-0.4B-LoRA-r64 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="socius/Smoltaur-0.4B-LoRA-r64", max_seq_length=2048, )
- Xet hash:
- ab827e0bb4a7a6a30d15912bc88beeffd7ddfeda1f57ea193fdbfc9f0e745d36
- Size of remote file:
- 14.6 kB
- SHA256:
- 716d83e48b4ef0ecc739db256420344d8764b568cd2fec53ea128d8653e43804
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