Instructions to use nicolasramos/odooclaw-vision 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 nicolasramos/odooclaw-vision 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 nicolasramos/odooclaw-vision:Q8_0 # Run inference directly in the terminal: llama cli -hf nicolasramos/odooclaw-vision:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nicolasramos/odooclaw-vision:Q8_0 # Run inference directly in the terminal: llama cli -hf nicolasramos/odooclaw-vision:Q8_0
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 nicolasramos/odooclaw-vision:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf nicolasramos/odooclaw-vision:Q8_0
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 nicolasramos/odooclaw-vision:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nicolasramos/odooclaw-vision:Q8_0
Use Docker
docker model run hf.co/nicolasramos/odooclaw-vision:Q8_0
- LM Studio
- Jan
- Ollama
How to use nicolasramos/odooclaw-vision with Ollama:
ollama run hf.co/nicolasramos/odooclaw-vision:Q8_0
- Unsloth Studio
How to use nicolasramos/odooclaw-vision 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 nicolasramos/odooclaw-vision 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 nicolasramos/odooclaw-vision to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nicolasramos/odooclaw-vision to start chatting
- Pi
How to use nicolasramos/odooclaw-vision with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nicolasramos/odooclaw-vision:Q8_0
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": "nicolasramos/odooclaw-vision:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use nicolasramos/odooclaw-vision with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nicolasramos/odooclaw-vision:Q8_0
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 "nicolasramos/odooclaw-vision:Q8_0" \ --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"
- Docker Model Runner
How to use nicolasramos/odooclaw-vision with Docker Model Runner:
docker model run hf.co/nicolasramos/odooclaw-vision:Q8_0
- Lemonade
How to use nicolasramos/odooclaw-vision with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nicolasramos/odooclaw-vision:Q8_0
Run and chat with the model
lemonade run user.odooclaw-vision-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use nicolasramos/odooclaw-vision with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nicolasramos/odooclaw-vision:Q8_0
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 nicolasramos/odooclaw-vision:Q8_0
Run Hermes
hermes
- Atomic Chat
odooclaw-vision
Vision model for OdooClaw — on-premise document extraction (invoices, delivery notes) with no cloud dependencies.
Base model: zai-org/GLM-OCR (0.9B params, MIT license) — the best quality-per-parameter OCR of 2026, exceptional at tables and structured documents. GGUF conversion by ggml-org.
Files
| File | Size | Description |
|---|---|---|
odooclaw-vision-Q5_K_M.gguf |
~610 MB | Main model (Q5_K_M quantization) |
mmproj-odooclaw-vision-Q8_0.gguf |
~462 MB | Multimodal projector (mmproj) for llama.cpp |
Usage with llama.cpp
llama-server \
-m odooclaw-vision-Q5_K_M.gguf \
--mmproj mmproj-odooclaw-vision-Q8_0.gguf \
--host 0.0.0.0 --port 8093 \
-c 8192 --parallel 1 --temp 0.0 \
--alias odooclaw-vision
OpenAI-compatible endpoint:
curl http://localhost:8093/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "odooclaw-vision",
"messages": [{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
{"type": "text", "text": "Extract all text from this invoice document, preserving table structure."}
]}],
"temperature": 0,
"max_tokens": 2048
}'
OdooClaw pipeline architecture
Invoice PDF → odooclaw-vision (image → structured text)
→ odooclaw-light (text → JSON: partner, vat, ref, date, total, lines)
→ business rules (validation: reverse charge, currency, sanity checks)
→ account_dynamic_rules (Odoo rules: account, analytics, taxes)
→ vendor bill in Odoo
Performance
- CPU (N100, 4 cores): ~1-5 min per page at 96 dpi
- Quality: totals, partners, dates and line items extracted correctly from real production invoices (SIEPER, utilities, telecom)
License
MIT. Commercial use allowed.
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Model tree for nicolasramos/odooclaw-vision
Base model
zai-org/GLM-OCR