Articles tagged "Qwen"

**[Software Mansion's React Native ExecuTorch](https://docs.swmansion.com/react-native-executorch/) shipped a `useLLM` hook** that gives React Native apps **native on-device LLM integration**. **Supported models**: quantized Qwen (2.5 / 3 / 3.5), Llama 3.2, Hammer 2.1, Phi 4 Mini, SmolLM 2, LFM2.5 (vision), and **[Gemma 4](../columns/gemma-4-technical-report-2026-07) (vision + audio)**. **Two operating modes**: **Functional/Stateless** (developers manage conversation history via `generate()` + `response`; tool calling and chat config don't apply) and **Managed/Stateful** (`sendMessage()` maintains conversation state, parses tool calls, and runs callbacks automatically). **Key features**: token batching (groups tokens before re-render), tool calling (model invokes external functions via tool schemas with automatic parsing and callbacks), vision-language / audio multimodal inputs, generation control (temperature / top-p / repetition penalty / mid-stream interruption), and **JSONSchema- or Zod-backed structured output**. **Use cases**: on-device chatbots without server dependencies, privacy-first conversation UIs, in-app function calling (calendar events, flashlight, etc.), multimodal features (image analysis, audio transcription). **Positioning**: implementation-level evidence that **cutting-edge open-weight LLMs like [Gemma 4's encoder-free 12B](../columns/gemma-4-technical-report-2026-07) and [Qwen 3.6-35B](../columns/qwen36-35b-a3b-uncensored-abliterated-2026-07) natively fit into iOS / Android apps** — a major deliverable from the React Native ecosystem.

A 2026 Q2 performance benchmark of Gemma 4 (E2B / E4B / 26B MoE / 31B Dense) against the major open-weights peers — Llama 4, Qwen 3.5, Mistral, and DeepSeek — across MMLU-Pro, GPQA, HumanEval, MATH-500, and MT-Bench. Adds throughput (tokens / s), memory efficiency (quality per GB VRAM), cost per million tokens, Japanese-language performance, native function calling, and Apache 2.0 / MIT / commercial-use licensing as of May 2026, plus a use-case selection matrix for in-house LLM, edge AI, coding assistants, and RAG.