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LFM2.5-350M

LiquidAI

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. - **Best-in-class performance**: A 350M model rivaling much larger models, bringing high-quality AI to your pocket. - **Fast edge inference**: 313 tok/s decode on AMD CPU, 188 tok/s on Snapdragon Gen4. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM. - **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning. Find more information about LFM2.5-350M in our blog post.

Text generation
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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. This pre-trained checkpoint is only recommended for tasks that require heavy fine-tuning, like language-specific (e.g., Japanese) or domain-specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post-training approaches. LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list.

Text generation
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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/LFM2.5-350M The Quantization-Aware Distillation (QAD) checkpoint is available as `LFM2.5-350M-QAD-Q4_0.gguf`.

Text generation
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GGUF build of `LiquidAI/LFM2.5-8B-A1B-DSpark` for **llama.cpp** (DSpark speculative decoding is in mainline, ggml-org/llama.cpp #25173). This is a standalone **draft sidecar**: it carries only the drafter (5 attention layers, rank-256 Markov head, confidence head, block size 9). Token embeddings and the LM head are shared from the target model at load time, so it must be paired with a LFM2.5-8B-A1B-GGUF target file. Draft quantization changes speed only marginally (the drafter is a small share of each cycle); choose by memory budget. The target model quant is the main speed/quality lever and is independent of this file. The block size is read from the sidecar metadata (n-max is clamped to it). Speculative decoding is **exact**: the target verifies every proposed token, so greedy output equals the target alone; per-response `timings` report `draft_n` / `draft_n_accepted`.

Text generation
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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/LFM2.5-8B-A1B

Text generation
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LFM2.5-Audio-1.5B is Liquid AI's updated end-to-end audio foundation model. Key improvements include a custom, LFM based audio detokenizer, `llama.cpp` compatible GGUFs for CPU inference, and better ASR and TTS performance. LFM2.5-Audio is an end-to-end multimodal speech and text language model, and as such does not require separate ASR and TTS components. Designed with low latency and real time conversation in mind, at only 1.5 billion parameters LFM2.5-Audio enables seamless conversational interaction, achieving capabilities on par with much larger models. Our model consists of a pretrained LFM2.5 model as its multimodal backbone, along with a FastConformer based audio encoder to handle continuous audio inputs, and an RQ-transformer generating discrete tokens coupled with a lightweight audio detokenizer for audio output. LFM2.5-Audio supports two distinct generation routines, each suitable for a set of tasks. Interleaved generation enables real-time speech-to-speech conversational chatbot capabilities, where audio generation latency is key. Sequential generation is suited for non-conversational tasks such as ASR or TTS, and allows the model to switch generated modality on the fly.

Audio processing
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Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B Runners are built from https://github.com/ggml-org/llama.cpp/pull/18641. It's WIP and will take time to land in upstream.

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This repository contains GGUF quantizations of LiquidAI/LFM2.5-Audio-1.5B-JP for use with llama.cpp. - Got questions or want to connect? Join our Discord community - If you are interested in custom solutions with edge deployment, please contact our sales team. The code in this repository and associated weights are licensed under the LFM Open License v1.0.

Audio processing
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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