Instructions to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf",
	filename="BF16/LLaMA-4-Maverick-17B-split-00001-of-00018.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

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 AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

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 AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

Use Docker

docker model run hf.co/AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

LM Studio
Jan
Ollama
How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with Ollama:
```
ollama run hf.co/AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
```

Unsloth Studio

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-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 AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-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 AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf to start chatting

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

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": "AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

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 AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with Docker Model Runner:
```
docker model run hf.co/AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16
```

Lemonade

How to use AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf:BF16

Run and chat with the model

lemonade run user.llama-4-maverick-17b-128e-instruct-f16-gguf-BF16

List all available models

lemonade list

LLaMA 4 Maverick 17B 128E Instruct (GGUF, Non-Quantized)

This is a non-quantized GGUF conversion of the original meta-llama/Llama-4-Maverick-17B-128E-Instruct model.
It has been converted for compatibility with inference libraries that use the GGUF format, such as llama.cpp, llamacpp-python, llamafile, and Ollama.

Model Details

Architecture: LLaMA 4
Model Type: Mixture of Experts (MoE) - 128 Experts
Parameters: 17 Billion (base), larger when expanded with MoE
Quantization: None (float16 precision)
Format: .gguf (non-quantized)

Intended Use

Research and evaluation purposes
Fine-tuning or quantization into lower-bit formats (q4, q5) for efficient inference
Deployment on high-memory systems (256 GB RAM or >512 GB VRAM recommended)

Notes

This model is extremely large. The unquantized GGUF file size is approximately 801 GB.
Running this model requires high-end hardware, preferably with multiple GPUs or extremely large VRAM.
A quantized version (q4_k_m, q5_0) will be released separately for easier local inference.

License

This model is distributed under the Meta Llama 4 license and the same terms apply.
Users must have signed the Meta license agreement to access and use this model.

Acknowledgments

Original model weights: Meta AI
GGUF conversion: Done using llama.cpp tooling.

Disclaimer

This is a direct format conversion.
No fine-tuning, evaluation, or modification has been performed beyond reformatting into GGUF.

Use responsibly and within the bounds of the Meta Llama 4 license.

Downloads last month: 16

GGUF

Model size

401B params

Architecture

llama4

Hardware compatibility

16-bit

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Model tree for AaronimusPrime/llama-4-maverick-17b-128e-instruct-f16-gguf

Base model

meta-llama/Llama-4-Maverick-17B-128E

Finetuned

meta-llama/Llama-4-Maverick-17B-128E-Instruct

Quantized

(18)

this model