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🚀 Let’s transform LLMs into encoders 🚀
🚀 Let’s transform LLMs into encoders 🚀
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Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨
Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨
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Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡
Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡
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What’s New?
What’s New?
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🔹Converted Llama & Qwen decoders to advanced encoders
🔹Converted Llama & Qwen decoders to advanced encoders
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🔹Improved GLiNER architecture to be able to work with rotary positional encoding
🔹Improved GLiNER architecture to be able to work with rotary positional encoding
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🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models 
🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models 
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🔥 Check it out: 
🔥 Check it out: 
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New models: 
New models: 
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https://huggingface.co/collections/knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
collection
knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
https://huggingface.co/collections/knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
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GLiNER package: 
GLiNER package: 
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https://github.com/urchade/GLiNER
https://github.com/urchade/GLiNER
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GLiClass package: 
GLiClass package: 
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https://github.com/Knowledgator/GLiClass
https://github.com/Knowledgator/GLiClass
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💻 Read our blog for more insights, and stay tuned for what’s next!
💻 Read our blog for more insights, and stay tuned for what’s next!
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link
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
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🚀 Let’s transform LLMs into encoders 🚀

Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨

Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡

What’s New?
🔹Converted Llama & Qwen decoders to advanced encoders
🔹Improved GLiNER architecture to be able to work with rotary positional encoding
🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models 

🔥 Check it out: 

New models: https://huggingface.co/collections/knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e

GLiNER package: https://github.com/urchade/GLiNER

GLiClass package: https://github.com/Knowledgator/GLiClass

💻 Read our blog for more insights, and stay tuned for what’s next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966

👀
John6666