Instructions to use nandhakumar7/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nandhakumar7/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "nandhakumar7/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use nandhakumar7/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 nandhakumar7/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 nandhakumar7/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nandhakumar7/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="nandhakumar7/outputs", max_seq_length=2048, )
- Xet hash:
- d76f01bd60499cd47011b17d136757f0d630a76a88260677e9934c4de7562112
- Size of remote file:
- 5.11 kB
- SHA256:
- d6601c554304cadbb67c363c3f848816993528a21e78e82e2d52f175088ebd57
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