How to use from
Pi
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apolo13x/Cosmos-Reason2-2B-GGUF:
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": "apolo13x/Cosmos-Reason2-2B-GGUF:"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

Cosmos-Reason2-2B-GGUF

GGUF quantizations of nvidia/Cosmos-Reason2-2B for use with llama.cpp and compatible tools.

About the Model

NVIDIA Cosmos Reason 2 is an open, 2B-parameter reasoning vision-language model (VLM) for physical AI and robotics. It is post-trained from Qwen3-VL-2B-Instruct and understands space, time, and fundamental physics.

Key capabilities:

  • Physical AI reasoning with spatio-temporal understanding
  • Object detection with 2D/3D point localization and bounding boxes
  • Long-context understanding up to 256K input tokens
  • Video analytics, data curation, and robot planning

For full details, see the original model card.

Quantization Details

File Quant Size
Cosmos-Reason2-2B-F16.gguf F16 3.8 GB
Cosmos-Reason2-2B-Q8_0.gguf Q8_0 2.1 GB
Cosmos-Reason2-2B-Q4_K_M.gguf Q4_K_M 1.2 GB
mmproj-Cosmos-Reason2-2B-F16.gguf F16 782 MB

Note: The vision encoder (mmproj) is kept at F16 precision.

How to Use

llama-server -hf Kbenkhaled/Cosmos-Reason2-2B-GGUF:Q8_0
llama-server -hf Kbenkhaled/Cosmos-Reason2-2B-GGUF:F16
llama-server -hf Kbenkhaled/Cosmos-Reason2-2B-GGUF:Q4_K_M
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qwen3vl
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