Instructions to use HyzeAI/HyzeMini-Q3_K_L-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HyzeAI/HyzeMini-Q3_K_L-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HyzeAI/HyzeMini-Q3_K_L-GGUF",
	filename="hyzemini-q3_k_l.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HyzeAI/HyzeMini-Q3_K_L-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 HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

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 HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
./llama-cli -hf HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

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 HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

Use Docker

docker model run hf.co/HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

LM Studio
Jan

vLLM

How to use HyzeAI/HyzeMini-Q3_K_L-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HyzeAI/HyzeMini-Q3_K_L-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HyzeAI/HyzeMini-Q3_K_L-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

Ollama
How to use HyzeAI/HyzeMini-Q3_K_L-GGUF with Ollama:
```
ollama run hf.co/HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
```

Unsloth Studio

How to use HyzeAI/HyzeMini-Q3_K_L-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 HyzeAI/HyzeMini-Q3_K_L-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 HyzeAI/HyzeMini-Q3_K_L-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HyzeAI/HyzeMini-Q3_K_L-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use HyzeAI/HyzeMini-Q3_K_L-GGUF with Docker Model Runner:
```
docker model run hf.co/HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L
```

Lemonade

How to use HyzeAI/HyzeMini-Q3_K_L-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HyzeAI/HyzeMini-Q3_K_L-GGUF:Q3_K_L

Run and chat with the model

lemonade run user.HyzeMini-Q3_K_L-GGUF-Q3_K_L

List all available models

lemonade list

Hyze Logo

HyzeMini

A quantized lightweight text-generation model by Hyze AI

🔗 Chat with all models • 📘 HyzeAcademy • 🧠 HyzeNote (NotebookLM alternate)

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo HyzeAI/HyzeMini-Q3_K_L-GGUF --hf-file hyzemini-q3_k_l.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo HyzeAI/HyzeMini-Q3_K_L-GGUF --hf-file hyzemini-q3_k_l.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo HyzeAI/HyzeMini-Q3_K_L-GGUF --hf-file hyzemini-q3_k_l.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo HyzeAI/HyzeMini-Q3_K_L-GGUF --hf-file hyzemini-q3_k_l.gguf -c 2048

Downloads last month: 1

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

3-bit

Model tree for HyzeAI/HyzeMini-Q3_K_L-GGUF

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HyzeAI/HyzeMini

Quantized

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