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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
Input Audio→ Output Audio
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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.

Category pending
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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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- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.

Embeddings & similarity
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- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.

Embeddings & similarity
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LFM2.5-Embedding-350M is a dense bi-encoder for fast multilingual retrieval. It produces a single vector per document — the smallest, fastest index — for reliable cross-lingual search across 11 languages. Make requests to embed queries and documents, and rank by cosine similarity (note the asymmetric `query: ` / `document: ` prompt prefixes)

Embeddings & similarity
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**LFM2.5-Encoder** is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: Both are masked language models with full bidirectional attention, designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on-device. > [!NOTE] > 💻 **Demos**: We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs in a CPU-only Hugging Face space: > - **Zero-shot prompt routing** — define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass. > - **Zero-shot policy linting** — check text against your company's rules, written as free text. It scores every token against every rule in one pass. > - **Spell checking** — correct misspellings token by token. > - **PII detection** — spot and remove 40 kinds of personal information across 16 languages. > - **Masked-diffusion text generation** — bonus: run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.

Fill-mask
Input Text→ Output Text
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**LFM2.5-Encoder** is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: Both are masked language models with full bidirectional attention, designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on-device. > [!NOTE] > 💻 **Demos**: We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs in a CPU-only Hugging Face space: > - **Zero-shot prompt routing** — define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass. > - **Zero-shot policy linting** — check text against your company's rules, written as free text. It scores every token against every rule in one pass. > - **Spell checking** — correct misspellings token by token. > - **PII detection** — spot and remove 40 kinds of personal information across 16 languages. > - **Masked-diffusion text generation** — bonus: run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.

Fill-mask
Input Text→ Output Text
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