Instructions to use DesilDev/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DesilDev/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2-0.5b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "DesilDev/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use DesilDev/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 DesilDev/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 DesilDev/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DesilDev/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DesilDev/outputs", max_seq_length=2048, )
- Xet hash:
- 40e4035863accfab7e360a50511ea748a0410fe722d055e18d33020ad14f3a20
- Size of remote file:
- 5.18 kB
- SHA256:
- 9f955a7316d2d16e7c2457530123af6127abf0903e820f726e9d4c94199ece1d
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