Qwen/Qwen3-1.7B

Name: Qwen/Qwen3-1.7B
Author: Alibaba

Run locally on Apple devices with Mirai

Type: local
From: Alibaba
Quantization: No
Parameters: 1.7B
Size: 3.2 GB
Source

Qwen3-1.7B is a compact 1.7 billion parameter causal language model from Alibaba's Qwen team, part of the third-generation Qwen series. Despite its small footprint, it incorporates the architectural and training advances of the full Qwen3 lineup, making it a capable option for resource-constrained deployments.

Architecture & Specs

Built on a dense transformer architecture with 28 layers, the model uses Grouped Query Attention (16 query heads, 8 key-value heads) and supports a context length of 32,768 tokens. Of its 1.7B total parameters, 1.4B are non-embedding. It is derived from the Qwen3-1.7B-Base via both pretraining and post-training stages.

Thinking & Non-Thinking Modes

A standout feature of Qwen3 models is the ability to seamlessly switch between thinking mode and non-thinking mode within a single model. In thinking mode, the model engages in step-by-step reasoning (wrapped in `<think>...</think>` blocks), improving performance on math, coding, and logic tasks. In non-thinking mode, it behaves like a standard instruction-following model for fast, general-purpose dialogue. Users can toggle between modes via a simple `enable_thinking` flag or inline `/think` and `/no_think` tags during conversation.

Key Capabilities

Reasoning: Enhanced logical reasoning, mathematical problem-solving, and code generation
Agent & Tool Use: Precise integration with external tools, including MCP-based configurations and Qwen-Agent for agentic workflows
Multilingual: Supports 100+ languages and dialects with strong instruction-following and translation abilities
Conversational Quality: Improved alignment for creative writing, role-playing, and multi-turn dialogue

Deployment

Qwen3-1.7B is compatible with Hugging Face Transformers (v4.51.0+), SGLang, vLLM, Ollama, LMStudio, llama.cpp, and other popular inference frameworks. Its small size makes it well-suited for edge deployment, local applications, and latency-sensitive use cases where larger models are impractical.

1	import Uzu
2
3	public func runChat() async throws {
4	let engineConfig = EngineConfig.create()
5	let engine = try await Engine.create(config: engineConfig)
6
7	guard let model = try await engine.model(identifier: "Qwen/Qwen3-1.7B") else {
8	return
9	}
10	for try await update in try await engine.download(model: model).iterator() {
11	print("Download progress: \(update.progress())")
12	}
13
14	let messages = [
15	ChatMessage.system().withText(text: "You are a helpful assistant"),
16	ChatMessage.user().withText(text: "Tell me a short, funny story about a robot")
17	]
18	let session = try await engine.chat(model: model, config: .create())
19	let stream = await session.replyWithStream(input: messages, config: .create())
20	var message: ChatMessage? = nil
21	for try await update in stream.iterator() {
22	switch update {
23	case .replies(let replies):
24	message = replies.last?.message
25	case .error(let error):
26	print("Error: \(error)")
27	}
28	}
29	print("Text: \(message?.text() ?? "empty")")
30	}

Architecture & Specs

Thinking & Non-Thinking Modes

Key Capabilities

Deployment

Other local models from Alibaba