Instructions to use SansarK/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SansarK/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("h2oai/h2o-danube3-500m-base") model = PeftModel.from_pretrained(base_model, "SansarK/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use SansarK/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 SansarK/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 SansarK/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SansarK/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SansarK/outputs", max_seq_length=2048, )
- Xet hash:
- 24a91b04cdc9e7fa0eeefc784a6c2106368a1e2e5e758ea35f05c81b0bcf48cc
- Size of remote file:
- 5.11 kB
- SHA256:
- 723fbf8f3a505dbf1e4f289ca0e32672c1caa2032e7c131f1080b12f74abe747
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