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 Studio
How to use addyo07/vox-models with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for addyo07/vox-models to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for addyo07/vox-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for addyo07/vox-models to start chatting
- 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 @mariozechner/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"
addy-hypr4 commited on
Commit ·
d92eadb
1
Parent(s): b536905
Add DistilBERT Query Classifier model assets and update models manifest to v1.3.0
Browse files
classifier/distilbert-query-classifier/model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:5093f8d2e2252cd952e134231fb784809bc45598d278f89e56c8a320017fdeae
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size 135740553
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classifier/distilbert-query-classifier/tokenizer.json
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models_manifest.json
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"models_version": "1.
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"release_notes": [
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"Standardized canonical model directory structure and file names across all engines.",
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"MiniLM-L12-v2 integrated as primary multilingual embedding engine.",
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"BGE-M3 configured as fallback embedding engine.",
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"Nemotron 3.5, Qwen3 ASR, DeBERTa-v3 NLI, Supertonic 3, and Chatterbox TTS paths standardized."
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"models_version": "1.3.0",
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"release_notes": [
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"Standardized canonical model directory structure and file names across all engines.",
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"MiniLM-L12-v2 integrated as primary multilingual embedding engine.",
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"BGE-M3 configured as fallback embedding engine.",
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"DistilBERT Query Classifier integrated as primary memory intent routing engine.",
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"Nemotron 3.5, Qwen3 ASR, DeBERTa-v3 NLI, Supertonic 3, and Chatterbox TTS paths standardized."
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"total_size_bytes": 10219831741,
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"id": "distilbert_query_classifier",
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"name": "DistilBERT Query Classifier Engine",
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"category": "classifier",
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"version": "1.0.0",
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"files": [
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{
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"id": "classifier_model_quantized",
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"path": "classifier/distilbert-query-classifier/model_quantized.onnx",
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"size": 135740553,
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"sha256": "5093f8d2e2252cd952e134231fb784809bc45598d278f89e56c8a320017fdeae",
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"required": true
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"id": "classifier_tokenizer",
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"path": "classifier/distilbert-query-classifier/tokenizer.json",
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"size": 2919625,
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"sha256": "519ee3affedf69aac5333eb1963ce157b17c67e650f9a9903c561d2f47cf02bb",
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"required": true
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