Instructions to use 1024m/L2-MAP-RAND with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/L2-MAP-RAND with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("1024m/L2-MAP-RAND", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use 1024m/L2-MAP-RAND 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 1024m/L2-MAP-RAND 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 1024m/L2-MAP-RAND to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for 1024m/L2-MAP-RAND to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="1024m/L2-MAP-RAND", max_seq_length=2048, )
- Xet hash:
- ed452cdd91b2398666049d769cbd997d567efe19d8d5c296c9ff868ce2369231
- Size of remote file:
- 16.5 MB
- SHA256:
- 48a973c597df7c8281e21bb768c5e4feacb01352a260718bb6920986429d85fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.