EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Jul 28·5 min read
LFM2.5-Encoders for Fast Long-Context Inference on CPU
TL;DR
LFM2.5-Encoders are new models that outperform larger encoders on various benchmarks while offering fast inference on CPUs with an 8,192-token context. They enable efficient applications like intent routing and text classification.
✦ Why It Matters
Start integrating LFM2.5-Encoders into your applications for efficient long-context processing today.
Key Takeaways
- Utilize LFM2.5-Encoders for high-volume NLP tasks.
- Choose LFM2.5-Encoder-350M for maximum accuracy.
- Select LFM2.5-Encoder-230M for better speed on limited hardware.
- Fine-tune encoders for specific applications like classification or PII detection.
How It Works
LFM2.5-Encoders leverage a bidirectional attention mechanism, allowing each token to consider its surrounding context. They are trained using a two-stage process: first, they develop general language skills with a masked-language objective, and then they adapt to long contexts by extending their training to 8,192 tokens.
This architecture enables efficient processing of lengthy inputs while maintaining high accuracy.