Instructions to use jonathanjordan21/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jonathanjordan21/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "jonathanjordan21/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use jonathanjordan21/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 jonathanjordan21/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 jonathanjordan21/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jonathanjordan21/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jonathanjordan21/outputs", max_seq_length=2048, )
- Xet hash:
- 6753623280f4b08ac38528ec08555adc6abe5a57ca8e0a43d72cccb919340f92
- Size of remote file:
- 5.43 kB
- SHA256:
- 3ff1e63772e5755fa692dc4bceb26a37a3bb2257d63f93d35b049ca6fa42fa38
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