LFM2.5-24B-A2B: A Fast New Artificial Intelligence Model for Personal Devices
Liquid AI released LFM2.5-24B-A2B, a new artificial intelligence model built to reason and use tools quickly on local computers. It matches larger models in many tests while using less memory and running faster.
In short: Liquid AI released LFM2.5-24B-A2B, a new artificial intelligence model built to reason and use tools quickly on local computers. It matches larger models in many tests while using less memory and running faster.
Have you ever wanted an artificial intelligence assistant that thinks step-by-step and runs smoothly right on your own computer? A company called Liquid AI just released a new model designed to do exactly that, while keeping your data close to home.
What happened, in plain words
Liquid AI announced a new artificial intelligence model called LFM2.5-24B-A2B. Unlike older versions, this model always thinks before it answers, producing an explicit chain of thought before giving a final response. The creators trained the model using massive amounts of data and fine-tuned it through several stages, including teacher specialization and reinforcement learning. When tested against other models, LFM2.5-24B-A2B performed exceptionally well on tool-use and instruction-following tasks despite being smaller than its competitors.
Key points
- Built for reasoning and tools: The model always thinks before answering and excels at tasks that require following instructions and using external software tools.
- Fast performance on local hardware: It runs very quickly on everyday hardware like Apple and Ryzen chips, decoding many tokens per second while using minimal memory.
- Expanded memory and vocabulary: Its context window grows to handle much larger chunks of text at once, and its vocabulary is doubled to better understand non-Latin languages.
- Open for everyone to use: Both the base and final versions are open-weight and available on Hugging Face, ready to be paired with local software setups and agent harnesses.
Terms explained
- Tokens — Pieces of words that artificial intelligence models read and write, roughly equivalent to chunks of text or syllables. Example: Reading a short sentence requires processing a small handful of tokens.
- Context Window — The maximum amount of text an artificial intelligence model can look at and remember all at once during a conversation. Example: A larger context window lets you feed an entire book into the model instead of just a single paragraph.
- Inference — The process of an artificial intelligence model running its trained software to generate answers and respond to prompts. Example: When you type a question and the model immediately types back an answer, it is performing inference.
Why it matters
This technology gives developers and hobbyists a powerful tool to run smart assistants directly on personal hardware. Because it is fast and efficient, users can build local applications that reason through problems and use software tools without needing massive cloud servers.
What we still don't know
The source notes that larger models still hold an advantage on knowledge-heavy tests and complex coding tasks. Users needing deep encyclopedic knowledge or heavy programming help should pair this model with a web search or a retrieval system.
Based on reporting from Hugging Face Blog. This is an independent explainer, written in our own words with AI assistance; Hugging Face Blog has not reviewed or endorsed it. Read the original for the full details.