Instructions to use thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT 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 thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT 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 thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="thlurte/FastData-LM-1.5B-CodeMath-SLERP-SFT", max_seq_length=2048, )
- Xet hash:
- ef0c3ab7cb0462c2eef9b80dd1f96c52d75049d1a6507868fe6b58dfd85292f5
- Size of remote file:
- 73.9 MB
- SHA256:
- 2436e8bc624ff9d9e58212db4a7d0c422c2c13db3f76173875e73446cfb6e769
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