Instructions to use addyo07/vox-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 addyo07/vox-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 addyo07/vox-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf addyo07/vox-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 addyo07/vox-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf addyo07/vox-models:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf addyo07/vox-models:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf addyo07/vox-models:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf addyo07/vox-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf addyo07/vox-models:Q4_K_M
Use Docker
docker model run hf.co/addyo07/vox-models:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use addyo07/vox-models with Ollama:
ollama run hf.co/addyo07/vox-models:Q4_K_M
- Unsloth Desktop
- Pi
How to use addyo07/vox-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf addyo07/vox-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": "addyo07/vox-models:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use addyo07/vox-models with Docker Model Runner:
docker model run hf.co/addyo07/vox-models:Q4_K_M
- Lemonade
How to use addyo07/vox-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull addyo07/vox-models:Q4_K_M
Run and chat with the model
lemonade run user.vox-models-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use addyo07/vox-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 addyo07/vox-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 addyo07/vox-models:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use addyo07/vox-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf addyo07/vox-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 "addyo07/vox-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"
Commit History
14 days ago
-
feat(models): publish Qwen 3.5 0.8B, disfluency, ZipVoice and voices; relax required flags
addy-hypr4Adds the model blobs that the manifest already declared but the repo never contained, which made first-run fail on Android: - llm/qwen/qwen-3.5-0.8b-q4_k_m.gguf was listed in models_manifest.json but was never uploaded, so `llm/qwen/...` returned 404 and aborted the setup batch. sha256 bd258782e3... matches the manifest entry. - disfluency/ (DeBERTa v3 base + large) for the Phase 13 disfluency model. - tts/zipvoice/ for local zero-shot TTS. - voices/ reference clips. Required flags are relaxed so on-device first-run only pulls what genuinely cannot be deferred to a cloud provider: - ten_vad -> optional (silero_vad remains the mandatory VAD) - qwen_3_5_0_8b -> optional (remote LLM is a valid configuration) .gitattributes gains disfluency/**, tts/zipvoice/** and voices/** LFS rules. Without them the 8 MB DeBERTa tokenizer.json files and espeak ru_dict would be committed as raw git blobs; nli/** and embedding/** were already covered by equivalent catch-alls.e24e95b
-
Standardize model asset paths and update models manifest
addy-hypr4b536905
-
added llama3.2 q4 as default option
addy-hypr47b005b8
-
added new translit model , new llms added and updated manifest
addy-hypr48f99f92
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feat: restructure repo to match runtime, add missing piper voices, and update README/manifest
addy-hypr4c9658c4 -
feat: initial model upload with manifest
addy-hypr4dc9b7ec