- Vendor
- LiquidAI
- Quantization
- MLX 4-bit
- Parameters
- 2.6B
- Size
- 1.4 GB
$ brew install mirai$ mirai --model mlx-community/LFM2-2.6B-4bitBenchmarks
LFM 2
Apple M4 Max 128GB
0.51 s
lower is better ↓
uzu0.5.14vs
0.31.2vs
llama.cpp0.3.23LFM 2
Apple M4 Max 128GB
255 t/s
higher is better ↑
uzu0.5.14vs0.31.2vsllama.cpp0.3.23LFM 2
Apple M4 Max 128GB
1.55 GB
lower is better ↓
uzu0.5.14vs0.31.2vsllama.cpp0.3.23Benchmarked 7 Aug 2026
Integrate with SDK
https://github.com/trymirai/uzu-swift
| 1 | import Foundation |
| 2 | import Uzu |
| 3 | |
| 4 | public func runChat() async throws { |
| 5 | let engineConfig = EngineConfig.create() |
| 6 | let engine = try await Engine.create(config: engineConfig) |
| 7 | |
| 8 | guard let model = try await engine.model(identifier: "alibaba:qwen3.5:0.8b:mirai:mirai-m:4") else { |
| 9 | return |
| 10 | } |
| 11 | for try await update in try await engine.download(model: model).iterator() { |
| 12 | print(String(format: "\r\u{001B}[2KDownload progress: %.2f%%", update.progress() * 100), terminator: "") |
| 13 | fflush(stdout) |
| 14 | } |
| 15 | print() |
| 16 | |
| 17 | let messages = [ |
| 18 | ChatMessage.system().withText(text: "You are a helpful assistant"), |
| 19 | ChatMessage.user().withText(text: "Tell me a short, funny story about a robot") |
| 20 | ] |
| 21 | let session = try await engine.chat(model: model, config: .create()) |
| 22 | let stream = await session.replyWithStream(input: messages, config: .create()) |
| 23 | var message: ChatMessage? = nil |
| 24 | for try await update in stream.iterator() { |
| 25 | switch update { |
| 26 | case .replies(let replies): |
| 27 | let reply = replies.last |
| 28 | message = reply?.message |
| 29 | print("Generated tokens: \(reply?.stats.tokensCountOutput ?? 0)") |
| 30 | case .error(let error): |
| 31 | print("Error: \(error)") |
| 32 | } |
| 33 | } |
| 34 | print("Reasoning: \(message?.reasoning() ?? "empty")") |
| 35 | print("Text: \(message?.text() ?? "empty")") |
| 36 | } |
| 37 | |
Details
A 4-bit quantized version of LiquidAI's LFM2-2.6B, converted to Apple's MLX format for efficient on-device inference on Apple Silicon hardware. This community conversion was produced using `mlx-lm` v0.28.0.
Origin & Architecture
LFM2-2.6B is developed by Liquid AI as part of their LFM2 (Liquid Foundation Model) family, purpose-built for edge deployment. The model is compact at 2.6 billion parameters, and the 4-bit quantization further reduces memory footprint and accelerates inference — making it well-suited for local use on Mac devices without requiring cloud resources.
Multilingual Text Generation
The model supports text generation across eight languages:
- English, French, German, Spanish
- Arabic, Chinese, Japanese, Korean
This broad multilingual coverage in a small, quantized package makes it a practical choice for on-device applications that need to handle diverse language inputs.
Key Strengths
- Edge-optimized: Designed from the ground up for resource-constrained environments, combining a small parameter count with aggressive quantization.
- MLX-native: Runs natively on Apple Silicon via the MLX framework, leveraging unified memory and GPU acceleration on M-series chips.
- Low barrier to use: Compatible with the `mlx-lm` Python library, supporting chat templates and standard generation workflows out of the box.
Use Cases
LFM2-2.6B-4bit is a strong fit for local chatbots, lightweight multilingual assistants, text summarization, and any scenario where low-latency, private, on-device inference is preferred over cloud-based API calls. Its small size makes it especially appealing for developers prototyping on laptops or deploying to edge hardware.
> License: Released under the LFM 1.0 license from Liquid AI. Review the license terms before commercial use.