LFM2.5 2.6B model competitive with 4x larger models

LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with a 128K context window and agentic post-training.

- Best-in-class agent: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks.

- Agentic reinforcement learning: Trained inside the most popular agentic harnesses to improve compatibility.

- Efficient inference: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory.

Find more information about LFM2.5-2.6B in our blog post.

💻 Demos: Try LFM2.5-2.6B's agentic capabilities in a Hugging Face space without any setup: Research Agent in your browser: helps you research a specific question and generates a summary

🗒️ Model Details

Model Parameters Description

LFM2.5-2.6B-Base 2.6B Pre-trained base model for fine-tuning

LFM2.5-2.6B 2.6B Post-trained for agentic workloads

LFM2.5-2.6B is a general-purpose text-only model with the following features:

- Total parameters: 2.69B

- Number of layers: 30 (22 double-gated short convolution blocks + 8 GQA)

- Training budget: 34 trillion tokens

- Vocabulary size: 128,000

- Context length: 131,072 tokens

- Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish

- Generation parameters:

- temperature: 0.1

- top_k: 50

- repetition_penalty: 1.1

Model Description

LFM2.5-2.6B Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang.

LFM2.5-2.6B-GGUF Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage.