Instructions to use Nodmix/Nodmix-Q3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nodmix/Nodmix-Q3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nodmix/Nodmix-Q3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nodmix/Nodmix-Q3", dtype="auto") - llama-cpp-python
How to use Nodmix/Nodmix-Q3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Nodmix/Nodmix-Q3", filename="Nodmix-Q3.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Nodmix/Nodmix-Q3 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 Nodmix/Nodmix-Q3:Q4_K_S # Run inference directly in the terminal: llama cli -hf Nodmix/Nodmix-Q3:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Nodmix/Nodmix-Q3:Q4_K_S # Run inference directly in the terminal: llama cli -hf Nodmix/Nodmix-Q3:Q4_K_S
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 Nodmix/Nodmix-Q3:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf Nodmix/Nodmix-Q3:Q4_K_S
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 Nodmix/Nodmix-Q3:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nodmix/Nodmix-Q3:Q4_K_S
Use Docker
docker model run hf.co/Nodmix/Nodmix-Q3:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use Nodmix/Nodmix-Q3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nodmix/Nodmix-Q3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nodmix/Nodmix-Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nodmix/Nodmix-Q3:Q4_K_S
- SGLang
How to use Nodmix/Nodmix-Q3 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Nodmix/Nodmix-Q3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nodmix/Nodmix-Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Nodmix/Nodmix-Q3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nodmix/Nodmix-Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Nodmix/Nodmix-Q3 with Ollama:
ollama run hf.co/Nodmix/Nodmix-Q3:Q4_K_S
- Unsloth Studio
How to use Nodmix/Nodmix-Q3 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 Nodmix/Nodmix-Q3 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 Nodmix/Nodmix-Q3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Nodmix/Nodmix-Q3 to start chatting
- Pi
How to use Nodmix/Nodmix-Q3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nodmix/Nodmix-Q3:Q4_K_S
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": "Nodmix/Nodmix-Q3:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Nodmix/Nodmix-Q3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nodmix/Nodmix-Q3:Q4_K_S
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 Nodmix/Nodmix-Q3:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Nodmix/Nodmix-Q3 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nodmix/Nodmix-Q3:Q4_K_S
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 "Nodmix/Nodmix-Q3:Q4_K_S" \ --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 Nodmix/Nodmix-Q3 with Docker Model Runner:
docker model run hf.co/Nodmix/Nodmix-Q3:Q4_K_S
- Lemonade
How to use Nodmix/Nodmix-Q3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nodmix/Nodmix-Q3:Q4_K_S
Run and chat with the model
lemonade run user.Nodmix-Q3-Q4_K_S
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)Nodmix-Q4
Nodmix is the latest generation of large language models in Nodmix IQ series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Nodmix delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support
Model Files
| File Name | Size | Quantization | Format | Description |
|---|---|---|---|---|
Nodmix_Q4.F32.gguf |
16.1 GB | FP32 | GGUF | Full precision (float32) version |
Nodmix_4B.BF16.gguf |
8.05 GB | BF16 | GGUF | BFloat16 precision version |
Nodmix_4B.F16.gguf |
8.05 GB | FP16 | GGUF | Float16 precision version |
Nodmix_4B.Q3_K_M.gguf |
2.08 GB | Q3_K_M | GGUF | 3-bit quantized (K M variant) |
Nodmix_4B.Q3_K_S.gguf |
1.89 GB | Q3_K_S | GGUF | 3-bit quantized (K S variant) |
Nodmix_4B.Q4_K_M.gguf |
2.5 GB | Q4_K_M | GGUF | 4-bit quantized (K M variant) |
Nodmix_4B.Q4_K_S.gguf |
2.38 GB | Q4_K_S | GGUF | 4-bit quantized (K S variant) |
Nodmix_4B.Q5_K_M.gguf |
2.89 GB | Q5_K_M | GGUF | 5-bit quantized (K M variant) |
Nodmix_4B.Q8_0.gguf |
4.28 GB | Q8_0 | GGUF | 8-bit quantized |
.gitattributes |
2.02 kB | — | — | Git LFS tracking file |
config.json |
31 B | — | — | Configuration placeholder |
README.md |
3.6 kB | — | — | Model documentation |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
- Downloads last month
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Nodmix/Nodmix-Q3", filename="", )