Instructions to use LiquidAI/LFM2.5-Encoder-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-Encoder-230M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LiquidAI/LFM2.5-Encoder-230M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-Encoder-230M", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("LiquidAI/LFM2.5-Encoder-230M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 261912962da6838813162db73fac85bcf7ef3ab1a29ea4245797c283bbbf11f9
- Size of remote file:
- 919 MB
- SHA256:
- e2365f4805d311697d46c9aa326e50862466dab0b62c94b67c22b61aa779e45d
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