Instructions to use AtomicChat/SenseNova-Loop-9B-Preview-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 AtomicChat/SenseNova-Loop-9B-Preview-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 AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

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 AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/SenseNova-Loop-9B-Preview-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": "AtomicChat/SenseNova-Loop-9B-Preview-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Ollama
How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

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": "AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M
```

Lemonade

How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SenseNova-Loop-9B-Preview-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/SenseNova-Loop-9B-Preview-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 AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

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 AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/SenseNova-Loop-9B-Preview-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M

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 "AtomicChat/SenseNova-Loop-9B-Preview-GGUF:Q4_K_M" \
  --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-GGUF

GGUF quantizations of sensenova/SenseNova-Loop-9B-Preview — a looped multimodal model (looped decoder + manifold-constrained hyper-connections) from SenseNova. By AtomicChat.

New architecture — needs a patched llama.cpp. SenseNova-Loop is a sensenova_loop model: a Qwen3.5-class text backbone whose layers 4-27 are executed twice (56 layer computations from 32 stored layers) with two-stream manifold-constrained hyper-connections (mHC, Sinkhorn routing). Upstream llama.cpp, LM Studio and Ollama do not support this architecture yet, so these files will not load on a stock build. They were produced and verified with a llama.cpp build that adds SenseNova-Loop support, on top of ggml-org/llama.cpp commit 5047748f11 (upstream support PR pending). These are text-only GGUFs for now; the vision tower (mmproj) is a follow-up.

Files

Baseline preset quants (no importance matrix, no layout tuning) for a fast first release; improved quants to follow. The mHC routers are kept in F32 (routing is precision-sensitive).

File	bits/weight	Size on disk	Role
`SenseNova-Loop-9B-Preview-BF16.gguf`	16.0	16.7 GB	reference / highest fidelity
`SenseNova-Loop-9B-Preview-Q8_0.gguf`	~8.5	8.9 GB	near-lossless, fits comfortably in 16 GB
`SenseNova-Loop-9B-Preview-Q6_K.gguf`	~6.6	6.9 GB	high quality
`SenseNova-Loop-9B-Preview-Q5_K_M.gguf`	~5.7	6.0 GB	balanced
`SenseNova-Loop-9B-Preview-Q4_K_M.gguf`	~4.9	5.2 GB	smallest, runs on ~8 GB-class GPUs

Architecture notes

Text backbone is Qwen3.5-class: hybrid attention (three Gated-DeltaNet linear-attention layers per one full-attention layer), QK-norm, attention output gate, interleaved M-RoPE with partial_rotary_factor 0.25, dense SwiGLU FFN, vocab 248320.
Looped schedule: loop_start=4, loop_end=28, executed twice; each visit keeps its own attention KV and GDN recurrent state.
mHC: two parallel residual streams over the looped region, with a dense router (sigmoid pre/post gates + a Sinkhorn-normalized doubly-stochastic mix) replacing the plain residual add. Router weights are F32.
MTP draft block and the vision tower are not included in these text GGUFs.

Running

Build a llama.cpp with SenseNova-Loop support, then:

llama-cli -m SenseNova-Loop-9B-Preview-Q6_K.gguf -ngl 99 -c 4096 -st \
  -p "What is the capital of France? Answer in one short sentence."

The chat template (with thinking) is embedded in the GGUF. Verified greedy output on BF16: coherent reasoning, code and multilingual generations (e.g. the train-speed word problem resolves to 80 km/h; "capital of France" -> Paris).

Atomic Chat

Atomic Chat support: in progress (the engine needs this architecture; port underway).

Credits & license

Base model: sensenova/SenseNova-Loop-9B-Preview, Apache License 2.0 — see LICENSE. Quantized and published by AtomicChat.

Downloads last month: 365

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for AtomicChat/SenseNova-Loop-9B-Preview-GGUF

Base model

sensenova/SenseNova-Loop-9B-Preview

Quantized

(5)

this model

Collection including AtomicChat/SenseNova-Loop-9B-Preview-GGUF

SenseNova Loop

Collection

1 item • Updated about 10 hours ago