Instructions to use Rekname/voicenotes-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Rekname/voicenotes-models with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Rekname/voicenotes-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf Rekname/voicenotes-models:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Rekname/voicenotes-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf Rekname/voicenotes-models:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Rekname/voicenotes-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Rekname/voicenotes-models:Q4_K_M
Use Docker
docker model run hf.co/Rekname/voicenotes-models:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Rekname/voicenotes-models with Ollama:
ollama run hf.co/Rekname/voicenotes-models:Q4_K_M
- Unsloth Desktop
- Pi
How to use Rekname/voicenotes-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Rekname/voicenotes-models:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Rekname/voicenotes-models:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Rekname/voicenotes-models with Docker Model Runner:
docker model run hf.co/Rekname/voicenotes-models:Q4_K_M
- Lemonade
How to use Rekname/voicenotes-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Rekname/voicenotes-models:Q4_K_M
Run and chat with the model
lemonade run user.voicenotes-models-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Rekname/voicenotes-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Rekname/voicenotes-models:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Rekname/voicenotes-models:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Rekname/voicenotes-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Rekname/voicenotes-models:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Rekname/voicenotes-models:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Модели VoiceNotes
Зеркало моделей для офлайн-приложения VoiceNotes (стенограммы, субтитры и конспекты аудиозаписей). Файлы не изменялись и распространяются на условиях исходных лицензий.
| Каталог | Модель | Лицензия | Источник |
|---|---|---|---|
gigaam |
GigaAM-v3 e2e RNNT (распознавание русской речи) | MIT | https://github.com/salute-developers/GigaAM |
whisper/small |
Whisper small (многоязычное распознавание, быстрее) | MIT | https://huggingface.co/Systran/faster-whisper-small |
whisper/large-v3-turbo |
Whisper large-v3-turbo (многоязычное распознавание, точнее) | MIT | https://huggingface.co/dropbox-dash/faster-whisper-large-v3-turbo |
pyannote/community-1 |
pyannote speaker-diarization-community-1 (диаризация) | CC-BY-4.0 | https://huggingface.co/pyannote/speaker-diarization-community-1 |
sherpa |
pyannote segmentation-3.0 в ONNX (быстрая диаризация) | MIT | https://github.com/k2-fsa/sherpa-onnx |
sherpa |
WeSpeaker ResNet34-LM в ONNX (голосовые отпечатки) | CC-BY-4.0 | https://github.com/wenet-e2e/wespeaker |
llm |
Qwen3-4B-Instruct-2507 Q4_K_M (конспекты) | Apache-2.0 | https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507 |
Атрибуция (CC-BY-4.0):
- pyannote speaker-diarization-community-1 © pyannoteAI / Hervé Bredin, CC-BY-4.0.
- WeSpeaker ResNet34-LM © WeNet community, обучена на VoxCeleb, CC-BY-4.0.
- Downloads last month
- 9
Hardware compatibility
Log In to add your hardware
4-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support