Instructions to use Dukuru/firstmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dukuru/firstmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Dukuru/firstmodel") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Dukuru/firstmodel 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 Dukuru/firstmodel 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 Dukuru/firstmodel to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Dukuru/firstmodel to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Dukuru/firstmodel", max_seq_length=2048, )
- Xet hash:
- 7244eff82e8df333f01ae062d6ca95ca45e8f8a4869f585be8ddb7d3f9a22370
- Size of remote file:
- 97.3 MB
- SHA256:
- 8b30ca019fe9acec395615486f84978f2fa0f7f94569220a1f1e5a8c5323aff2
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