Instructions to use RadAlienware/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RadAlienware/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/phi-3-mini-4k-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "RadAlienware/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use RadAlienware/outputs 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 RadAlienware/outputs 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 RadAlienware/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RadAlienware/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="RadAlienware/outputs", max_seq_length=2048, )
- Xet hash:
- b2fb048c5f6345f01a4ec6e18c71d167b5a7535632e20f9688107eb3de9e93d1
- Size of remote file:
- 5.37 kB
- SHA256:
- d1dfc1e56e1e53423e0720c6adf295ba6efc7a419e6df2fdc6aaad1e30418df7
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