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

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

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.

Downloads last month: 13

GGUF

Model size

0.9B params

Architecture

glm4

Hardware compatibility

5-bit

Model tree for nicolasramos/odooclaw-vision

Base model

zai-org/GLM-OCR

Quantized

(31)

this model