Instructions to use Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Nimbus-Labs/Nimbus-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nimbus-Labs/Nimbus-4B-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": "Nimbus-Labs/Nimbus-4B-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/Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M

Ollama
How to use Nimbus-Labs/Nimbus-4B-GGUF with Ollama:
```
ollama run hf.co/Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nimbus-Labs/Nimbus-4B-GGUF to start chatting

How to use Nimbus-Labs/Nimbus-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nimbus-Labs/Nimbus-4B-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": "Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Nimbus-Labs/Nimbus-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nimbus-Labs/Nimbus-4B-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 "Nimbus-Labs/Nimbus-4B-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"

Docker Model Runner
How to use Nimbus-Labs/Nimbus-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M
```

Lemonade

How to use Nimbus-Labs/Nimbus-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nimbus-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-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 Nimbus-Labs/Nimbus-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

--- language: - en license: other license_name: nimbus-layered-open-source license_link: https://huggingface.co/Nimbus-Labs/Nimbus-4B-GGUF/blob/main/LICENSES.md library_name: gguf pipeline_tag: text-generation base_model: Nimbus-Labs/Nimbus-4B base_model_relation: quantized tags: - nimbus - qwen3.5 - coding - reasoning - local - gguf

Nimbus-4B GGUF

Text-only llama.cpp artifacts for Nimbus-4B. Q5_K_M is the quality-first local default; Q4_K_M is listed only where it actually exists.

Choose a Nimbus model

Model	Best fit	Transformers	GGUF	MLX
Nimbus-2B	Fast drafting and focused edits	`Nimbus-Labs/Nimbus-2B`	`Nimbus-Labs/Nimbus-2B-GGUF`	`Nimbus-Labs/Nimbus-2B-MLX-5bit`
Nimbus-4B	Balanced implementation and debugging	`Nimbus-Labs/Nimbus-4B`	`Nimbus-Labs/Nimbus-4B-GGUF`	`Nimbus-Labs/Nimbus-4B-MLX-5bit`
Nimbus-9B v2.1	Deeper coding and reasoning	`Nimbus-Labs/Nimbus-9B-v2.1`	`Nimbus-Labs/Nimbus-9B-v2.1-GGUF`	`Nimbus-Labs/Nimbus-9B-v2.1-MLX-5bit`

The adjacent assets/nimbus-family-footprint.json contains the plotted values. Download size is not runtime memory: context cache and runtime buffers require additional capacity.

Downloads

File	Role	Bytes	SHA-256
`Nimbus-4B-BF16.gguf`	BF16 reference	8,424,392,960 (8.42 GB)	`aec0fd8c767f67522ade75e453cdc9c2b8cbf16fdfa134cbbe6d20554f2cdb6b`
`Nimbus-4B-Q4_K_M.gguf`	Memory-first	2,708,803,840 (2.71 GB)	`f404660f5fb11fba99112b02c02e215945b48a2b981c26d05f6d3afd2d30e474`
`Nimbus-4B-Q5_K_M.gguf`	Quality-first default	3,074,986,240 (3.07 GB)	`def18cfa32028c76f8976a8856139440c601b305e71da5d4a13f0104f6994e5f`

llama.cpp

Validated release runtime: llama.cpp b10007. Native thinking uses the supplied Qwen/Ornith chat template and DeepSeek-style reasoning parsing.

llama-server --model Nimbus-4B-Q5_K_M.gguf --ctx-size 65536 --n-gpu-layers all --reasoning-format deepseek

Evaluation

The released Q5_K_M artifact was evaluated in direct mode on the full HumanEval and MBPP suites with llama.cpp b10007, one answer per task, temperature 0.6, top-p 0.95, top-k 20, and seed 42.

Benchmark	Passed	Total	pass@1
HumanEval	121	164	73.8%
HumanEval+	112	164	68.3%
MBPP	285	378	75.4%
MBPP+	232	378	61.4%

The adjacent assets/nimbus-4b-evalplus.json is the machine-readable source for this chart.

Provenance and scope

Quantized from Nimbus-Labs/Nimbus-4B
Foundation: Qwen3.5
GGUF files are text-only; the Transformers repository retains the multimodal components.
Do not infer memory fit from file size alone; context KV cache and runtime buffers require additional memory.

Licenses and notices

See LICENSES.md, THIRD_PARTY_NOTICES.md, and LICENSES/Apache-2.0.txt.

Project

Nimbus8 Â· Transformers checkpoint

Downloads last month: 1,042

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

16-bit

Collection including Nimbus-Labs/Nimbus-4B-GGUF

Nimbus — Local Coding Models

Collection

Official local coding models: 2B for speed, 4B for balance, and 9B v2.1 for deeper reasoning, with BF16 and GGUF releases. • 6 items • Updated 16 days ago