Instructions to use instilux/luni-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use instilux/luni-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="instilux/luni-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("instilux/luni-base") model = AutoModelForMaskedLM.from_pretrained("instilux/luni-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- masked-language-modeling
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# LUNI-base
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A ModernBERT-based masked language model pretrained on Luxembourgish (+ augmented Luxembourgish), following the Ettin recipe (see here: https://huggingface.co/jhu-clsp/ettin-encoder-68m)
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## Model Details
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- **Architecture:** ModernBERT (encoder)
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- **Size:**
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- **Vocabulary:** 50,368 tokens (BPE, GPTNeoXTokenizerFast)
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- **Context length:** 1,024 tokens
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- **Language:** Luxembourgish (`lb`/`ltz`)
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- masked-language-modeling
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---
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# LUNI-base
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A ModernBERT-based masked language model pretrained on Luxembourgish (+ augmented Luxembourgish), following the Ettin recipe (see here: https://huggingface.co/jhu-clsp/ettin-encoder-68m)
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## Model Details
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- **Architecture:** ModernBERT (encoder)
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- **Size:** base
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- **Vocabulary:** 50,368 tokens (BPE, GPTNeoXTokenizerFast)
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- **Context length:** 1,024 tokens
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- **Language:** Luxembourgish (`lb`/`ltz`)
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