Instructions to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF 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 lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Use Docker

docker model run hf.co/lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Llama new
LM Studio
Jan

vLLM

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-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": "lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-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/lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Ollama
How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16
```
Unsloth Desktop

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16
```

Lemonade

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Run and chat with the model

lemonade run user.SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

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 lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16

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 "lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF:BF16" \
  --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"

SenseNova-Loop-9B-Preview — multimodal GGUFs

CPU-quantized GGUF tiers of sensenova/SenseNova-Loop-9B-Preview. Each main GGUF includes the text model and its MTP draft block. For image input, pair any tier with the shared BF16 multimodal projector (mmproj/ below).

Files

Main GGUF	Target BPW	Size
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ2_XXS.gguf`	2.30	2.95 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ2_XS.gguf`	2.50	2.95 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ2_S.gguf`	2.70	3.65 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ2_M.gguf`	2.90	3.85 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_XXS.gguf`	3.00	3.65 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_XS.gguf`	3.25	4.31 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_S.gguf`	3.50	4.31 GiB
`quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_M.gguf`	3.75	4.73 GiB

Shared image projector:

mmproj/SenseNova-Loop-9B-Preview-MMProj-BF16.gguf (0.86 GiB)

The BPW values are allocator targets, not guarantees of achieved BPW or exact file size. The same BF16 mmproj works with all eight main-model tiers.

Modality and runtime

The main GGUF retains the SenseNova loop execution plan, mHC routing tensors, and the MTP head at block 56. The vision tower and image projection are in the separate BF16 mmproj file. Image input requires a llama.cpp build with SenseNova-Loop and Qwen3VL projector support; MTP speculative decoding also requires runtime support for this architecture.

Example image prompt:

llama-mtmd-cli \
  -m quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_S.gguf \
  --mmproj mmproj/SenseNova-Loop-9B-Preview-MMProj-BF16.gguf \
  --image picture.jpg \
  --prompt "Describe this image." \
  -ngl 0

Example MTP-enabled text generation:

llama-cli \
  -m quant/SenseNova-Loop-9B-Preview-GSQ-RCO-IQ3_S.gguf \
  --spec-type draft-mtp \
  --prompt "Write a short greeting." \
  -n 32 \
  -ngl 0

CPU smoke tests

All eight tiers passed GGUF structural checks and text-generation smoke tests.
The IQ3_S MTP draft-context test accepted 31 of 139 draft tokens (22.3%).
The IQ3_S image test loaded the shared BF16 projector and read the headline “MEN WALK ON MOON” from the test image.

Provenance

sensenova/SenseNova-Loop-9B-Preview BF16 checkpoint
  revision 90dab7a3bf91922f5775be8deb6a290c128cfb90
  → full text+MTP BF16 GGUF, including block 56
  → CPU per-tensor quantization with an MTP-aware importance matrix
  → separate shared BF16 Qwen3VL mmproj

The converter is based on llama.cpp revision fad174ecd8bf22746a626c58e67e193f095aaa46 with local SenseNova-Loop support. Calibration uses the workspace's quantization_calibration.txt corpus; the importance matrix was generated with NextN/MTP collection enabled. “GSQ-RCO style” refers to this workspace's local per-tensor allocation workflow and is not a bit-exact reproduction of the upstream GSQ/RCO algorithms.

All conversion, quantization, and smoke testing are CPU-only. See the upstream model card for intended use and license details.

Downloads last month: -

GGUF

Model size

9B params

Architecture

sensenova_loop

Hardware compatibility

2-bit

3-bit

Model tree for lavendercomputing/SenseNova-Loop-9B-Preview-GSQ-RCO-GGUF

Base model

sensenova/SenseNova-Loop-9B-Preview

Quantized

(9)

this model