Instructions to use itztheking/FMAX-testrun-embed-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itztheking/FMAX-testrun-embed-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="itztheking/FMAX-testrun-embed-1")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("itztheking/FMAX-testrun-embed-1") model = AutoModel.from_pretrained("itztheking/FMAX-testrun-embed-1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use itztheking/FMAX-testrun-embed-1 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 itztheking/FMAX-testrun-embed-1 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 itztheking/FMAX-testrun-embed-1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for itztheking/FMAX-testrun-embed-1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="itztheking/FMAX-testrun-embed-1", max_seq_length=2048, )
(Trained with Unsloth)
Browse files
model-00012-of-00014.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4969ad2a3d9dc3819ce07b9fadc94da34c68bd0ce4f4e9291f765586402c1186
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size 4876059416
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