LFM2
PulseAugur coverage of LFM2 — every cluster mentioning LFM2 across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Liquid AI releases fast, CPU-friendly bidirectional encoders with 8K context
Liquid AI has released two new open-weight bidirectional encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. These models are built on the LFM2 hybrid backbone and support an 8,192-token context window, designe…
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LiquidAI releases LFM2.5 multilingual bidirectional encoders
LiquidAI has released two new multilingual bidirectional encoders, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, built on the LFM2 architecture. These models are designed for on-device efficiency and fine-tuning for vari…
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New 'WOOFER' Metric Reveals Surprising LLM Performance Scores
A user has developed a "WOOFER" metric to evaluate Large Language Model (LLM) performance using a "Probe_prompt." This metric has yielded surprising results, with some models scoring unexpectedly low, such as # bigpickl…
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Tutorial shows LFM2 fine-tuning with QLoRA and DPO
This tutorial demonstrates how to fine-tune the LFM2 model using QLoRA and Direct Preference Optimization (DPO) on Google Colab. It covers loading the base LFM2 model with 4-bit quantization, preparing a dataset for sup…
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Liquid AI releases LFM2-24B-A2B, an efficient 24B parameter MoE model
Liquid AI has released an early checkpoint of its LFM2-24B-A2B model, a sparse Mixture of Experts (MoE) architecture with 24 billion total parameters and 2 billion active parameters per token. This model demonstrates th…