Instructions to use LiamVisionary/swarm-sovereign-26b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiamVisionary/swarm-sovereign-26b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LiamVisionary/swarm-sovereign-26b",
	filename="swarm-sovereign-26b-Q4_K_M.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 LiamVisionary/swarm-sovereign-26b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiamVisionary/swarm-sovereign-26b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiamVisionary/swarm-sovereign-26b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiamVisionary/swarm-sovereign-26b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiamVisionary/swarm-sovereign-26b: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 LiamVisionary/swarm-sovereign-26b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiamVisionary/swarm-sovereign-26b: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 LiamVisionary/swarm-sovereign-26b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiamVisionary/swarm-sovereign-26b:Q4_K_M

Use Docker

docker model run hf.co/LiamVisionary/swarm-sovereign-26b:Q4_K_M

LM Studio
Jan

vLLM

How to use LiamVisionary/swarm-sovereign-26b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiamVisionary/swarm-sovereign-26b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiamVisionary/swarm-sovereign-26b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiamVisionary/swarm-sovereign-26b:Q4_K_M

Ollama
How to use LiamVisionary/swarm-sovereign-26b with Ollama:
```
ollama run hf.co/LiamVisionary/swarm-sovereign-26b:Q4_K_M
```

Unsloth Studio

How to use LiamVisionary/swarm-sovereign-26b 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 LiamVisionary/swarm-sovereign-26b 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 LiamVisionary/swarm-sovereign-26b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LiamVisionary/swarm-sovereign-26b to start chatting

Docker Model Runner
How to use LiamVisionary/swarm-sovereign-26b with Docker Model Runner:
```
docker model run hf.co/LiamVisionary/swarm-sovereign-26b:Q4_K_M
```

Lemonade

How to use LiamVisionary/swarm-sovereign-26b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiamVisionary/swarm-sovereign-26b:Q4_K_M

Run and chat with the model

lemonade run user.swarm-sovereign-26b-Q4_K_M

List all available models

lemonade list

Swarm Sovereign 26B GGUF

Swarm Sovereign 26B is a Gemma 4 26B-A4B IT based chat model prepared for local inference and LM Studio/llama.cpp usage.

This release contains both a Q4_K_M GGUF build and the merged Hugging Face safetensors build.

GGUF build:

File: swarm-sovereign-26b-Q4_K_M.gguf
Base model: google/gemma-4-26B-A4B-it
Architecture: Gemma 4
Quantization: Q4_K_M
Approximate file size: 16 GB
LM Studio estimated memory at 4096 context / 100% GPU offload: 16.44 GiB
Identity: Swarm Sovereign

Hugging Face / Transformers build:

Files: model-00001-of-00011.safetensors through model-00011-of-00011.safetensors, plus tokenizer/config files
Approximate directory size: 48 GB
Use this option for Transformers/PEFT-style local loading or downstream conversion workflows.

Local usage

llama.cpp

llama-cli \
  -m swarm-sovereign-26b-Q4_K_M.gguf \
  -ngl 99 \
  -c 4096 \
  --jinja \
  -p "What is your name? Answer in one sentence."

LM Studio

Import or download the GGUF in LM Studio, then load it as a local model. The local test identifier used during release validation was:

swarm-sovereign-26b

Validation

Local validation on Apple Silicon / LM Studio:

Prompt: What is your name? Answer in one sentence.
Answer: My name is Swarm Sovereign.

LM Studio memory estimate at 4096 context with full GPU offload:

Estimated GPU Memory:   16.44 GiB
Estimated Total Memory: 16.44 GiB

Want to build and manage an entire private swarm of agents?

Want to build and manage an entire private swarm of agents all with shared memory, skills, and single setup?

Check out: https://hivemindos.liamvisionary.com X: @TheHivemindOS

Notes

This model is intended for local experimentation and private agent workflows. Test thoroughly for your use case before deploying in production.

Downloads last month: 45

Safetensors

Model size

26B params

Tensor type

BF16

Model tree for LiamVisionary/swarm-sovereign-26b

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Quantized

(225)

this model

Quantizations

2 models