Instructions to use RJTPP/scot0500s-magistral-small-2509-24b-REF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJTPP/scot0500s-magistral-small-2509-24b-REF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RJTPP/scot0500s-magistral-small-2509-24b-REF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use RJTPP/scot0500s-magistral-small-2509-24b-REF 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 RJTPP/scot0500s-magistral-small-2509-24b-REF 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 RJTPP/scot0500s-magistral-small-2509-24b-REF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RJTPP/scot0500s-magistral-small-2509-24b-REF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="RJTPP/scot0500s-magistral-small-2509-24b-REF", max_seq_length=2048, )
- Xet hash:
- 69fed7824e208ee074cba7c9e5bb8e6ca44f574504f92214ce319deb23791811
- Size of remote file:
- 406 MB
- SHA256:
- d15f85f92facef51d074fbfd77643b290500623f09d4c1140dd11f136aaf3e16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.