Fine-Tune LFM2.5 Encoder for Multi-Label Text Classification

This walkthrough shows how to fine-tune Liquid AI's LFM2.5 Encoder for multi-label document classification using the Liquid AI Cookbook. The implementation keeps the pretrained bidirectional encoder backbone, mean-pools the contextual token representations, and adds a linear classification head that returns a probability for every known label in one forward pass. Unlike generative classification pipelines, this approach does not require a decoding loop, prompt templates, generated JSON, or output-repair logic. The resulting classifier is easier to benchmark, threshold, and run repeatedly in high-volume pipelines. 🔗 Links: • GitHub repo: https://github.com/Liquid4All/cookbook/tree/main/examples/lfm-encoder-classification • Blog post: https://www.liquid.ai/blog/lfm2-5-encoders Connect with Liquid AI: • Careers: https://www.liquid.ai/careers • Hugging Face: https://huggingface.co/LiquidAI • Discord: https://discord.com/invite/liquid-ai • X: https://x.com/LiquidAI • LinkedIn: https://www.linkedin.com/company/liquid-ai-inc/ • GitHub: https://github.com/Liquid4All/cookbook • Substack: https://liquidai.substack.com/