Instructions to use Srinivasmec26/MindSlate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Srinivasmec26/MindSlate with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("fill-in-model-name") model.load_adapter("Srinivasmec26/MindSlate", set_active=True) - Transformers
How to use Srinivasmec26/MindSlate with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Srinivasmec26/MindSlate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Srinivasmec26/MindSlate 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 Srinivasmec26/MindSlate 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 Srinivasmec26/MindSlate to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Srinivasmec26/MindSlate to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Srinivasmec26/MindSlate", max_seq_length=2048, )
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# MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management
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language:
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license: apache-2.0
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datasets:
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- Srinivasmec26/Educational-Flashcards-for-Global-Learners
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- Srinivasmec26/Task-Reminders-for-Education-and-Professionals
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- Srinivasmec26/Multidisciplinary-Educational-Summaries
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- Srinivasmec26/Structured-Todo-Lists-for-Learning-and-Projects
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library_name: adapter-transformers
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# MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management
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