Instructions to use Nimbus-Labs/Nimbus-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Nimbus-Labs/Nimbus-2B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Nimbus-Labs/Nimbus-2B-GGUF", filename="Nimbus-2B-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-2B-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-2B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nimbus-Labs/Nimbus-2B-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-2B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nimbus-Labs/Nimbus-2B-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-2B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Nimbus-Labs/Nimbus-2B-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-2B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nimbus-Labs/Nimbus-2B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Nimbus-Labs/Nimbus-2B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Nimbus-Labs/Nimbus-2B-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-2B-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-2B-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-2B-GGUF:Q4_K_M
- Ollama
How to use Nimbus-Labs/Nimbus-2B-GGUF with Ollama:
ollama run hf.co/Nimbus-Labs/Nimbus-2B-GGUF:Q4_K_M
- Unsloth Studio
How to use Nimbus-Labs/Nimbus-2B-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-2B-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-2B-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-2B-GGUF to start chatting
- Pi
How to use Nimbus-Labs/Nimbus-2B-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-2B-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-2B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Nimbus-Labs/Nimbus-2B-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-2B-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-2B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Nimbus-Labs/Nimbus-2B-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-2B-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-2B-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-2B-GGUF with Docker Model Runner:
docker model run hf.co/Nimbus-Labs/Nimbus-2B-GGUF:Q4_K_M
- Lemonade
How to use Nimbus-Labs/Nimbus-2B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nimbus-Labs/Nimbus-2B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Nimbus-2B-GGUF-Q4_K_M
List all available models
lemonade list
Nimbus-2B GGUF
Text-only llama.cpp artifacts for Nimbus-2B. 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-2B-BF16.gguf |
BF16 reference | 3,775,708,544 (3.78 GB) | 4c35e21bec421799ba27fccb4ed560a534b243076c05fefb386b95d9e140123a |
Nimbus-2B-Q4_K_M.gguf |
Memory-first | 1,274,396,032 (1.27 GB) | 25aefd6d16c6af14c87028e3edc958069e8515eb179727e12ba5c4180c46f995 |
Nimbus-2B-Q5_K_M.gguf |
Quality-first default | 1,411,120,512 (1.41 GB) | 25d813bfe0d655dfa3629322b332a3edd4d56c5d3b5f72a1fd245786b6770d03 |
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-2B-Q5_K_M.gguf --ctx-size 65536 --n-gpu-layers all --reasoning-format deepseek
Evaluation
No public score is claimed in this card yet. Results will be added only after the exact released artifact, complete task set, and scorer outputs are bound to a release manifest.
Provenance and scope
- Quantized from
Nimbus-Labs/Nimbus-2B - 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.
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