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Point to the correct model
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skills/local-ai-app-integration/SKILL.md

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| Coding assistant | `Qwen2.5-Coder-7B-Instruct-GGUF` | `llamacpp` | Strong code, runs on iGPU |
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| Vision / multimodal chat | `Gemma-4-E2B-it-GGUF` | `llamacpp` | Small multimodal default |
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| NPU-first on Ryzen AI | `Llama-3.2-3B-Instruct-Hybrid` | `ryzenai-llm` | XDNA2 NPU on Windows |
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| Speech-to-text | `Whisper-Large-v3-Turbo` | `whispercpp` | Best quality/speed |
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| NPU speech-to-text (Ryzen AI) | `Whisper-Medium` | `flm` | XDNA2 NPU on Windows; `flm` must also load an LLM alongside ASR |
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| CPU Speech-to-text | `Whisper-Large-v3-Turbo` | `whispercpp` | Best quality/speed |
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| NPU speech-to-text | `whisper-v3-turbo-FLM` | `flm` | XDNA2 NPU on Windows; `flm` must also load an LLM alongside ASR |
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| Text-to-speech | `kokoro-v1` | `kokoro` | CPU-only, low latency |
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| Image generation | `SDXL-Turbo` | `sd-cpp` | Single-step generation |
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skills/local-ai-app-integration/reference.md

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### Speech-to-text on NPU (`flm` recipe)
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For NPU-accelerated transcription on Ryzen AI, use the `flm` (FastFlowLM)
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recipe with `Whisper-Medium`. XDNA2 NPU, Windows only.
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recipe with `whisper-v3-turbo-FLM`. XDNA2 NPU, Windows only.
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### Text-to-speech
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| Multimodal (vision) chat | `Gemma-4-E2B-it-GGUF` | 2.0 GB | `llamacpp` |
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| Hybrid NPU chat (Ryzen AI) | `Llama-3.2-3B-Instruct-Hybrid` | 2.0 GB | `ryzenai-llm` |
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| Speech-to-text | `Whisper-Large-v3-Turbo` | 1.6 GB | `whispercpp` |
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| NPU speech-to-text (Ryzen AI) | `Whisper-Medium` | 1.5 GB | `flm` (needs a concurrent LLM) |
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| NPU speech-to-text (Ryzen AI) | `whisper-v3-turbo-FLM` | 0.6 GB | `flm` (needs a concurrent LLM) |
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| Text-to-speech | `kokoro-v1` | 0.3 GB | `kokoro` |
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| Image generation | `SDXL-Turbo` | 6.9 GB | `sd-cpp` |
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walkthroughs/local-ai-app-integration.md

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npm run tauri dev
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```
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Once the window opens, press `Ctrl+Shift+D`, speak, and confirm transcription now runs through
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your local `lemond` instead of a cloud provider.
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your local device instead of a cloud provider.
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## Step 6 - (Optional) Going beyond
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