Instructions to use pkcpkc/gbert-large-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pkcpkc/gbert-large-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download pkcpkc/gbert-large-mlx --local-dir gbert-large-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
gbert-large-mlx
MLX conversion (float16) of deepset/gbert-large.
The masked-language-model head (cls.*) was removed, leaving the BERT encoder and pooler, for use with
mlx-embeddings and compatible servers.
Note: this is a raw encoder, not a sentence-embedding model; fine-tune for best similarity-search quality.
Credits
This is a derivative of deepset/gbert-large, released under the MIT license. All credit for the model goes to its original authors:
- Branden Chan (deepset)
- Stefan Schweter
- Timo Möller (deepset)
and to deepset, who trained and published it. Paper: Chan, Schweter & Möller, German's Next Language Model (COLING 2020).
I only converted the weights to MLX format; no further training was done.
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Model size
0.3B params
Tensor type
F16
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deepset/gbert-large