Instructions to use robertzty/Cosmos-Reason2-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use robertzty/Cosmos-Reason2-32B-GGUF with Cosmos:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

llama-cpp-python

How to use robertzty/Cosmos-Reason2-32B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="robertzty/Cosmos-Reason2-32B-GGUF",
	filename="Cosmos-Reason2-32B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use robertzty/Cosmos-Reason2-32B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf robertzty/Cosmos-Reason2-32B-GGUF: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 robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf robertzty/Cosmos-Reason2-32B-GGUF: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 robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use robertzty/Cosmos-Reason2-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "robertzty/Cosmos-Reason2-32B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "robertzty/Cosmos-Reason2-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M

Ollama
How to use robertzty/Cosmos-Reason2-32B-GGUF with Ollama:
```
ollama run hf.co/robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use robertzty/Cosmos-Reason2-32B-GGUF 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 robertzty/Cosmos-Reason2-32B-GGUF 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 robertzty/Cosmos-Reason2-32B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for robertzty/Cosmos-Reason2-32B-GGUF to start chatting

Pi new

How to use robertzty/Cosmos-Reason2-32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf robertzty/Cosmos-Reason2-32B-GGUF: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": "Cosmos-Reason2-32B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use robertzty/Cosmos-Reason2-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M
```

Lemonade

How to use robertzty/Cosmos-Reason2-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull robertzty/Cosmos-Reason2-32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Cosmos-Reason2-32B-GGUF-Q4_K_M

List all available models

lemonade list

Cosmos-Reason2-32B-GGUF / README.md

robertzty

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	---
	base_model: nvidia/Cosmos-Reason2-32B
	library_name: llama.cpp
	pipeline_tag: image-text-to-text
	tags:
	- gguf
	- qwen3-vl
	- cosmos
	- nvidia
	- multimodal
	- image-text-to-text
	- bf16
	- q4_k_m
	- q5_k_m
	- q8_0
	license: other
	license_name: nvidia-open-model-license
	license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
	---

	# Cosmos-Reason2-32B GGUF

	Pure GGUF conversion of `nvidia/Cosmos-Reason2-32B`.

	Built on NVIDIA Cosmos.

	## Files

	- `Cosmos-Reason2-32B-BF16.gguf`: BF16 text backbone GGUF.
	- `Cosmos-Reason2-32B-Q4_K_M.gguf`: smaller 4-bit text backbone GGUF for lower memory use.
	- `Cosmos-Reason2-32B-Q5_K_M.gguf`: balanced 5-bit text backbone GGUF with better quality than Q4.
	- `Cosmos-Reason2-32B-Q8_0.gguf`: larger 8-bit text backbone GGUF for higher quality.
	- `mmproj-Cosmos-Reason2-32B-F16.gguf`: F16 multimodal projector / vision GGUF.

	Use one text backbone file together with the `mmproj` file for multimodal inference.

	## Hardware estimates

	These are rough inference estimates for `llama.cpp` with batch size 1. Actual memory use depends on context length, image/video inputs, backend, and how many layers are offloaded to GPU.

	\| Text backbone \| File size \| Text + mmproj \| Suggested system RAM \| Suggested VRAM for mostly/full GPU offload \| Notes \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| --- \|
	\| `Q4_K_M` \| 19.8 GB \| 21.0 GB \| 32 GB minimum, 48 GB comfortable \| 24 GB tight, 32 GB comfortable \| Best first choice for local use. \|
	\| `Q5_K_M` \| 23.2 GB \| 24.4 GB \| 48 GB comfortable \| 32 GB comfortable \| Better quality than Q4 with moderate extra memory. \|
	\| `Q8_0` \| 34.8 GB \| 36.0 GB \| 64 GB comfortable \| 48 GB+ recommended \| Higher quality, much larger. \|
	\| `BF16` \| 65.5 GB \| 66.7 GB \| 96 GB+ recommended \| 80 GB+ or multi-GPU \| Original precision GGUF; not a practical default for most local machines. \|

	KV cache adds roughly 2 GiB per 8k text tokens at fp16 cache precision, before additional image/video token overhead. Reduce `--ctx-size` or use partial CPU/GPU offload if memory is tight.

	## Source

	Original model: https://huggingface.co/nvidia/Cosmos-Reason2-32B

	This GGUF conversion was produced with `llama.cpp` `convert_hf_to_gguf.py` from the original Hugging Face safetensors.

	## Usage

	Use one text backbone file together with the multimodal projector in `llama.cpp`:

	```bash
	llama-server \
	-m Cosmos-Reason2-32B-Q4_K_M.gguf \
	--mmproj mmproj-Cosmos-Reason2-32B-F16.gguf
	```

	BF16 and Q8_0 are large and may require CPU offload or a multi-GPU setup.

	## License

	Licensed by NVIDIA Corporation under the NVIDIA Open Model License.

	See `NOTICE` and the original model card for license terms and usage requirements.