Instructions to use Nimbus-Labs/Nimbus-9B-v2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nimbus-Labs/Nimbus-9B-v2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nimbus-Labs/Nimbus-9B-v2.1-GGUF",
	filename="Nimbus-9B-v2.1-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 Settings

llama.cpp

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nimbus-Labs/Nimbus-9B-v2.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Nimbus-Labs/Nimbus-9B-v2.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-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-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M

Ollama
How to use Nimbus-Labs/Nimbus-9B-v2.1-GGUF with Ollama:
```
ollama run hf.co/Nimbus-Labs/Nimbus-9B-v2.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-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-9B-v2.1-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-9B-v2.1-GGUF to start chatting

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-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-9B-v2.1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Nimbus-Labs/Nimbus-9B-v2.1-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-9B-v2.1-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-9B-v2.1-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-9B-v2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/Nimbus-Labs/Nimbus-9B-v2.1-GGUF:Q4_K_M
```

Lemonade

How to use Nimbus-Labs/Nimbus-9B-v2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nimbus-Labs/Nimbus-9B-v2.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nimbus-9B-v2.1-GGUF-Q4_K_M

List all available models

lemonade list

Nimbus-9B-v2.1 GGUF

Text-only llama.cpp artifacts for Nimbus-9B-v2.1. 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-9B-v2.1-BF16.gguf`	BF16 reference	17,920,696,672 (17.92 GB)	`47ef4cd04c506292785131fff06d5d5d502c082347a44380c81cfe3316054166`
`Nimbus-9B-v2.1-Q4_K_M.gguf`	Memory-first	5,629,108,576 (5.63 GB)	`a3db6956cc148f332dd1afdec5ba389efabe02cf73af6cf975866f5557a9d74f`
`Nimbus-9B-v2.1-Q5_K_M.gguf`	Quality-first default	6,467,969,376 (6.47 GB)	`84c8604a77bcccf850e2a89bf2f3a28d2d846bf11e5f8dbec3094fe31be6e4f6`

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-9B-v2.1-Q5_K_M.gguf --ctx-size 65536 --n-gpu-layers all --reasoning-format deepseek

Evaluation

The released Q5_K_M artifact was evaluated with llama.cpp b10007 on Vulkan, native thinking, one answer per task, temperature 0.6, top-p 0.95, top-k 20, seed 42, and the official network-disabled EvalPlus scorer. The base run allowed 32,768 completion tokens. Each original length-plus-empty nonresponse received exactly one preserved 60,000-token recovery attempt; there was no repeated resampling.

Benchmark	Passed	Total	pass@1
HumanEval	146	164	89.0%
HumanEval+	135	164	82.3%
MBPP	330	378	87.3%
MBPP+	277	378	73.3%

Three HumanEval and seven MBPP truncations recovered at the larger limit. Two HumanEval and three MBPP nonresponses remained zero-valued. “Plus” uses EvalPlus' official base-and-plus pass condition.

The adjacent assets/nimbus-9b-v2.1-evalplus.json is the machine-readable source for this chart.

Release landscape

This is a capability and packaging comparison, not a cross-model score claim. Other publishers' benchmarks use different harnesses, prompts, inference budgets, and hardware, so they are not mixed into the Nimbus EvalPlus chart.

Release	Parameters	Modality	Published context	Positioning
Nimbus-9B v2.1	9B	Multimodal Transformers; text-only GGUF	262,144 validated for this release	Local coding/reasoning; BF16, Q4_K_M, Q5_K_M with checksums
Qwen3.5-9B	9B	Multimodal	262,144 in current serving guidance	Foundation model; reasoning and tool use
Ornith-1.0-9B	9B	Multimodal	262,144 in current serving guidance	Immediate upstream focused on coding agents
Granite-3.3-8B-Instruct	8B	Text	128,000	General instruction, code, function calling, and RAG

Nimbus-9B v2.1 inherits its architecture and foundation capabilities from Qwen3.5 through Ornith. Nimbus' contribution is the post-training, release packaging, quantization, and exact evaluation reported above—not authorship of the foundation model.

Provenance and scope

Quantized from Nimbus-Labs/Nimbus-9B-v2.1
Foundation: Ornith-1.0-9B on Qwen3.5-9B
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.