Instructions to use TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset 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 TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset 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 TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="TurkishCodeMan/DeepSeek-R1-Turkish-Dialog-Dataset", max_seq_length=2048, )
- Xet hash:
- 85ad5c8944ff5c073c022c58ae980d26c3527c588935c913dd82f733e95103c6
- Size of remote file:
- 17.2 MB
- SHA256:
- d91915040cfac999d8c55f4b5bc6e67367c065e3a7a4e4b9438ce1f256addd86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.