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. LFM2.5-1.2B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-1.2B variants. It has the following features: 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.
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View details ↗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/LFM2.5-1.2B-Instruct The Quantization-Aware Distillation (QAD) checkpoint is available as `LFM2.5-1.2B-Instruct-QAD-Q4_0.gguf`.
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View details ↗**LFM2.5-1.2B-JP-202606** is our latest general purpose Japanese chat model, delivering significant improvements in knowledge, instruction following, math, code, and tool-use over both the models of comparable size and LFM2.5-1.2B-JP. It sets a new benchmark for state-of-the-art performance in Japanese language understanding. Ideal for developers building Japanese-language applications where cultural and linguistic nuance matter. **LFM2.5-1.2B-JP-202606** は、当社の最新の汎用日本語チャットモデルです。知識、指示追従、数学、コード、ツール使用の各領域において、同規模の他モデルおよび LFM2.5-1.2B-JP の双方を大幅に上回る改善を実現しています。日本語全般における最高水準のベンチマーク性能を発揮します。 文化的・言語的なニュアンスが重要となる日本語アプリケーションを構築する開発者に最適です。 JMMLU‑ProX JMMLU JCulture JGPQA Avg J‑MIFEval JFBench 1 Avg J‑GSM8K J‑MATH500 Avg JHumanEval+ J‑BFCLv3 2
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View details ↗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.
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View details ↗ONNX export of **LFM2.5-1.2B-JP-202606** for cross-platform deployment via ONNX Runtime, Transformers.js, and the WebGPU stack. Same weights, same chat template — just compiled into ONNX graphs at multiple precisions, including a WebGPU-friendly INT4 + FP16 mix. LiquidAI/LFM2.5-1.2B-JP-202606 は当社の汎用日本語チャットモデルです。本リポジトリはその ONNX エクスポートで、ONNX Runtime / Transformers.js / WebGPU での実行に対応しています。重みおよびチャットテンプレートは同一です。 > **`model_q4f16.onnx` is the recommended variant for WebGPU**: INT4 weights with FP16 scales, FP16 KV cache and conv state I/O, FP32 logits via an inserted Cast — the format Transformers.js targets for browser inference.
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View details ↗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.
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View details ↗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. LFM2.5-2.6B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-2.6B variants. It has the following features: 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.
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View details ↗**LFM2.5-DSpark** is a family of speculative-decoding draft models that adapt DSpark for the LFM2.5 architecture. They allow LFM2.5 models to run faster without degrading quality. This is a drafter for **`LiquidAI/LFM2.5-2.6B`**. In SGLang, decoding runs about 2.6× faster. It also runs on-device on Apple silicon through the Metal backend. Find more information about LFM2.5-DSpark in our blog post.
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View details ↗GGUF build of `LiquidAI/LFM2.5-2.6B-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 LFM2.5-2.6B-GGUF target file. Find more information about LFM2.5-DSpark in our blog post.
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