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LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-2.6B The Quantization-Aware Distillation (QAD) checkpoint is available as `LFM2.5-2.6B-QAD-Q4_0.gguf`.
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LFM2.5-2.6B is a compact multilingual model built on LiquidAI's hybrid architecture, combining convolutional and attention layers for efficient long-context processing.
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LFM2.5 is a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. - **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets. - **Fast edge inference**: Best throughput from low-cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5. - **Built for agentic tasks**: Distilled from LFM2.5-350M and refined with multi-stage reinforcement learning, making it well-suited for tool use and data extraction. Find more information about LFM2.5-230M in our blog post.
LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. Find more information about LFM2.5-230M in our blog post. - **Number of parameters**: 230M - **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks) - **Training budget**: 28T tokens - **Context length**: 32,768 tokens - **Vocabulary size**: 65,536 - **Knowledge cutoff**: Mid-2024 - **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-230M The Quantization-Aware Distillation (QAD) checkpoint is available as `LFM2.5-230M-QAD-Q4_0.gguf`.
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FP8 version of LFM2.5-24B-A2B for GPU inference. Linear weights are quantized to FP8 with 128×128 blocks and dynamic activation quantization. The embedding, MoE routing gates and convolution weights are kept in their original precision. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-24B-A2B
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-24B-A2B The sampling parameters are the ones recommended for the base model (its `generation_config.json`); each GGUF also carries them as `general.sampling.*` metadata, so llama.cpp applies them by default.
This model LiquidAI/LFM2.5-24B-A2B-MLX-4bit was converted to MLX format from LiquidAI/LFM2.5-24B-A2B using mlx-lm version **0.32.0**. Quantized to 4 bits per weight (group size 64, affine mode). Token embedding (tied to the output head) kept at 6 bits. MoE routing gates kept unquantized in float32. For base model details, capabilities, and recommended generation settings, see the original model card.
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