Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Communication & Collaboration
MLX Local Inference Stack
Full local AI inference stack on Apple Silicon Macs via MLX. Includes: LLM chat (Qwen3-14B, Gemma3-12B), speech-to-text ASR (Qwen3-ASR, Whisper), text embedd...
skill openclawclawhub Free
Full local AI inference stack on Apple Silicon Macs via MLX. Includes: LLM chat (Qwen3-14B, Gemma3-12B), speech-to-text ASR (Qwen3-ASR, Whisper), text embedd...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, README_CN.md, SKILL.md, references/asr-qwen3.md, references/asr-whisper.md, references/embedding-qwen3.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.2.0
Provenance
- Publisher
- bendusy
- Source page
- View original listing
- Canonical URL
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Documentation
MLX Local Inference Stack
Full local AI inference on Apple Silicon Macs. All services expose OpenAI-compatible APIs.
Services Overview
ServicePortAccessModelsLLM + Whisper + Embedding8787LAN (0.0.0.0)qwen3-14b, gemma-3-12b, whisper-large-v3-turbo, qwen3-embedding-0.6b/4bASR (Qwen3-ASR)8788localhost onlyQwen3-ASR-1.7B-8bitTranscribe Daemon—file-basedUses ASR + LLM LaunchAgents: com.mlx-server (8787), com.mlx-audio-server (8788), com.mlx-transcribe-daemon
Models
Model IDParamsBest Forqwen3-14b14B 4bitChinese, deep reasoning (built-in think mode)gemma-3-12b12B 4bitEnglish, code generation
API
curl -X POST http://localhost:8787/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-14b", "messages": [{"role": "user", "content": "Hello"}], "temperature": 0.7, "max_tokens": 2048 }' Add "stream": true for streaming.
Python
from openai import OpenAI client = OpenAI(base_url="http://localhost:8787/v1", api_key="unused") response = client.chat.completions.create( model="qwen3-14b", messages=[{"role": "user", "content": "Hello"}], temperature=0.7, max_tokens=2048 ) print(response.choices[0].message.content)
Qwen3 Think Mode
Qwen3 may include <think>...</think> chain-of-thought tags. Strip them: import re text = re.sub(r'<think>.*?</think>\s*', '', text, flags=re.DOTALL)
Model Selection Guide
ScenarioRecommendedChinese textqwen3-14bCantoneseqwen3-14bEnglish writinggemma-3-12bCode generationEitherDeep reasoningqwen3-14b (think mode)Quick Q&Agemma-3-12b
Qwen3-ASR (best for Chinese/Cantonese)
curl -X POST http://127.0.0.1:8788/v1/audio/transcriptions \ -F "file=@audio.wav" \ -F "model=mlx-community/Qwen3-ASR-1.7B-8bit" \ -F "language=zh"
Whisper (multilingual, 99 languages)
curl -X POST http://localhost:8787/v1/audio/transcriptions \ -F "file=@audio.wav" \ -F "model=whisper-large-v3-turbo"
ASR Model Comparison
Qwen3-ASR (port 8788)Whisper (port 8787)Chinese/CantoneseStrongAverageMultilingualNoYes (99 langs)LAN accessNo (localhost)YesLoadingOn-demandAlways loaded
Supported audio formats
wav, mp3, m4a, flac, ogg, webm
Long audio
Split into 10-min chunks first: ffmpeg -y -ss 0 -t 600 -i long.wav -ar 16000 -ac 1 chunk_000.wav
Models
Model IDSizeUse Caseqwen3-embedding-0.6b0.6B 4bitFast retrieval, low latencyqwen3-embedding-4b4B 4bitHigh-accuracy semantic matching
API
curl -X POST http://localhost:8787/v1/embeddings \ -H "Content-Type: application/json" \ -d '{"model": "qwen3-embedding-0.6b", "input": "text to embed"}'
Batch
curl -X POST http://localhost:8787/v1/embeddings \ -H "Content-Type: application/json" \ -d '{"model": "qwen3-embedding-4b", "input": ["text 1", "text 2"]}'
Default Model: PaddleOCR-VL-1.5-6bit
ItemValueModel IDpaddleocr-vl-6bitSpeed~185 t/sMemory~3.3 GBPromptOCR:
CLI
cd ~/.mlx-server/venv python -m mlx_vlm.generate \ --model mlx-community/PaddleOCR-VL-1.5-6bit \ --image image.jpg \ --prompt "OCR:" \ --max-tokens 512 --temp 0.0
Python
from mlx_vlm import generate, load from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config model, processor = load("mlx-community/PaddleOCR-VL-1.5-6bit") config = load_config("mlx-community/PaddleOCR-VL-1.5-6bit") prompt = apply_chat_template(processor, config, "OCR:", num_images=1) out = generate(model, processor, prompt, "image.jpg", max_tokens=512, temperature=0.0, verbose=False) print(out.text if hasattr(out, "text") else out)
Notes
Prompt must be exactly OCR: for PaddleOCR-VL temperature=0.0 for deterministic output RGBA images must be converted to RGB first Venv: ~/.mlx-server/venv
Model: Qwen3-TTS (cached, not auto-served)
ItemValueModelQwen3-TTS-12Hz-1.7B-CustomVoice-8bitMemory~2GBFeatureCustom voice cloning
CLI
~/.mlx-server/venv/bin/mlx_audio.tts.generate \ --model mlx-community/Qwen3-TTS-12Hz-1.7B-CustomVoice-8bit \ --text "你好，这是一段测试语音"
As API (via mlx_audio.server on port 8788)
curl -X POST http://127.0.0.1:8788/v1/audio/speech \ -H "Content-Type: application/json" \ -d '{ "model": "mlx-community/Qwen3-TTS-12Hz-1.7B-CustomVoice-8bit", "input": "你好世界" }' --output speech.wav
6. Transcribe Daemon — Automatic Batch Transcription
Drop audio files into ~/transcribe/ for automatic processing: Daemon detects file (polls every 15s) Phase 1: Transcribe via Qwen3-ASR → filename_raw.md Phase 2: Correct via Qwen3-14B LLM → filename_corrected.md Move results to ~/transcribe/done/
LLM Correction Rules
Fix homophone errors (的/得/地, 在/再) Preserve Cantonese characters (嘅、唔、咁、喺、冇、佢) Add punctuation and paragraphs Remove filler words
Supported formats
wav, mp3, m4a, flac, ogg, webm
Service Management
# LLM + Whisper + Embedding server (port 8787) launchctl kickstart -k gui/$(id -u)/com.mlx-server # ASR server (port 8788) launchctl kickstart -k gui/$(id -u)/com.mlx-audio-server # Transcribe daemon launchctl kickstart gui/$(id -u)/com.mlx-transcribe-daemon # Logs tail -f ~/.mlx-server/logs/server.log tail -f ~/.mlx-server/logs/mlx-audio-server.err.log tail -f ~/.mlx-server/logs/transcribe-daemon.err.log
Requirements
Apple Silicon Mac (M1/M2/M3/M4) Python 3.10+ with mlx, mlx-lm, mlx-audio, mlx-vlm Recommended: 32GB+ RAM for running multiple models
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- README_CN.md
- README.md
- references/asr-qwen3.md
- references/asr-whisper.md
- references/embedding-qwen3.md