Instructions to use ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle 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 ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle 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 ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ViswanthSai/SmolLM-3B-Code-Specialist-Combined-kaggle", max_seq_length=2048, )
- Xet hash:
- 2ca2b9dea658f5c581a2eb5380029bb57973abcd04f41ccec683c902f85953a2
- Size of remote file:
- 17.2 MB
- SHA256:
- 8c10e9a574c31d77726079f4378e8e0c4c6bbf4516989e2d58bee052976994a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.