Qwen 3
Qwen/Qwen3-14B-MLX-4bit- Vendor
- Alibaba
- Quantization
- MLX 4-bit
- Parameters
- 14B
- Size
- 7.3 GB
$ brew install mirai$ mirai --model Qwen/Qwen3-14B-MLX-4bitBenchmarks
Qwen 3
Apple M4 Max 128GB
2.69 s
lower is better ↓
uzu0.5.14vs
0.31.2vs
llama.cpp0.3.23Qwen 3
Apple M4 Max 128GB
56 t/s
higher is better ↑
uzu0.5.14vs0.31.2vsllama.cpp0.3.23Qwen 3
Apple M4 Max 128GB
8.81 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 Qwen3-14B, optimized for Apple Silicon via the MLX framework. This model brings the full capabilities of Qwen's latest-generation language model to Mac hardware with significantly reduced memory requirements.
What It Is
Qwen3-14B-MLX-4bit is a causal language model with 14.8 billion parameters (13.2B non-embedding), built on a 40-layer transformer architecture with grouped-query attention (40 Q heads, 8 KV heads). It supports a native context length of 32,768 tokens, extendable to 131,072 tokens via YaRN rope scaling. The base model, Qwen3-14B, was developed by the Qwen team at Alibaba and underwent both pretraining and post-training stages before being quantized to 4-bit precision for efficient MLX inference.
Key Capabilities
- Dual Thinking Modes: Seamlessly switch between a *thinking mode* for complex reasoning tasks (math, code, logic) and a *non-thinking mode* for fast, general-purpose conversation — all within a single model. Thinking mode wraps internal reasoning in `<think>...</think>` blocks before delivering a final answer.
- Strong Reasoning: Surpasses both QwQ (in thinking mode) and Qwen2.5 Instruct (in non-thinking mode) on mathematics, code generation, and commonsense reasoning benchmarks.
- Agent & Tool Use: First-class support for tool calling and agentic workflows, compatible with frameworks like Qwen-Agent and MCP server configurations.
- Multilingual: Supports 100+ languages and dialects, with robust multilingual instruction-following and translation capabilities.
- Human Preference Alignment: Tuned for creative writing, role-playing, multi-turn dialogue, and precise instruction following.
Usage Notes
Requires `mlx_lm ≥ 0.25.2` and `transformers ≥ 4.52.4`. Recommended sampling parameters differ by mode — thinking mode works best with Temperature 0.6 and TopP 0.95, while non-thinking mode favors Temperature 0.7 and TopP 0.8. Greedy decoding should be avoided in thinking mode.
Licensed under Apache 2.0.