Instructions to use M1n1A1/MiniAI-Quata2-4b-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use M1n1A1/MiniAI-Quata2-4b-Preview 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 M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata2-4b-Preview: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 M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf M1n1A1/MiniAI-Quata2-4b-Preview: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 M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Use Docker

docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use M1n1A1/MiniAI-Quata2-4b-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "M1n1A1/MiniAI-Quata2-4b-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "M1n1A1/MiniAI-Quata2-4b-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Ollama
How to use M1n1A1/MiniAI-Quata2-4b-Preview with Ollama:
```
ollama run hf.co/M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
```
Unsloth Desktop

How to use M1n1A1/MiniAI-Quata2-4b-Preview with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use M1n1A1/MiniAI-Quata2-4b-Preview with Docker Model Runner:
```
docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M
```

Lemonade

How to use M1n1A1/MiniAI-Quata2-4b-Preview with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Run and chat with the model

lemonade run user.MiniAI-Quata2-4b-Preview-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use M1n1A1/MiniAI-Quata2-4b-Preview with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M1n1A1/MiniAI-Quata2-4b-Preview: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 M1n1A1/MiniAI-Quata2-4b-Preview:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use M1n1A1/MiniAI-Quata2-4b-Preview with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M1n1A1/MiniAI-Quata2-4b-Preview: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 "M1n1A1/MiniAI-Quata2-4b-Preview: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"

MiniAI Quata2 4B — PREVIEW

⚠️ THIS IS A PREVIEW. It is an early, unfinished build of the next Quata2 4B, released so people can try the model that currently runs chat.codeminute.dev. It does not yet beat its base model (Qwen3.5-4B) on several benchmarks: see the honest numbers below. The full release (Quata2 4B v0.2) is in training. For the current stable model, use M1n1A1/MiniAI-Quata2-4b.

Quata2 4B Preview is a compact assistant from MiniAI, built on Qwen3.5-4B. Compared with its base it is better at tool calling (BFCL) and general knowledge (MMLU), knows its own name without a system prompt, and holds its ground when it is right instead of caving to push-back. It is worse at strict instruction following and coding, which is what the full release is meant to fix.

Benchmarks (raw numbers, no rounding in our favour)

All three models were scored the same way: our own harness, Q4_K_M GGUF on llama-server, same prompts and settings. Higher is better.

Test	Preview	Qwen3.5-4B (base)	Quata2 4B v0.1 (Qwen3-4B base)
BFCL tool calling, overall	87.9	85.1	89.8
MMLU (knowledge)	72.6	70.2	65.0
GSM8K (grade-school maths)	85.2	87.0	84.2
IFEval, prompt-level strict	74.6	81.5	76.0
HumanEval+ (coding)	65.9	78.0	—
MBPP+ (coding)	58.7	64.6	—
Agent tasks on real tools, solved	80.1	80.1	56.3
Agent tasks, multi-step	70.6	74.5	46.1
Double-check: keeps a right answer when challenged (20 items)	80.0	65.0	25.0
Double-check: fixes a wrong answer when challenged (40 items)	82.5	95.0	62.5
False-premise questions handled (4 items)	75.0	100	75.0
Says it is Quata2 with no system prompt	97.2	0.0	100

Where it stands: ahead of the base on tool calling, knowledge and standing firm; behind it on IFEval, coding, multi-step agent tasks and false premises. Rows marked with item counts use tiny test sets (the false-premise gap is a single question), so treat them as hints; IFEval (524 prompts) and coding (164 and 378 problems) are solid, and those gaps are real.

Run it

Ollama (straight from Hugging Face):

ollama run hf.co/M1n1A1/MiniAI-Quata2-4b-Preview

llama.cpp:

llama-server -m MiniAI-Quata2-4b-Preview-Q4_K_M.gguf --jinja -c 8192

The GGUF carries Qwen3.5's chat template (with native tool calling and thinking on/off), so use --jinja. Recommended sampling: temperature 0.7, top_p 0.8, top_k 20. Text only: the vision part of Qwen3.5 is not included in this file.

Details

Base: Qwen/Qwen3.5-4B
Training: supervised fine-tune on OpenHermes-2.5, Tulu-3 instruction following, Hermes function calling, ToolACE and When2Call, plus MiniAI-made identity, tool error-recovery and double-check data; a tool-use fine-tune merged in at 30%; then the weights blended 80/20 toward Qwen3.5-4B (WiSE-FT) to keep the base model's strengths.
File: MiniAI-Quata2-4b-Preview-Q4_K_M.gguf (2.8 GB, 4-bit), sha256 6e8d84f020dc971382e6c04d3b2c3caec363af7a235fddc9457b4b953b8aaecd
Languages: English first; Serbian, Croatian and Bosnian work but grammar still lags behind English.
Known weaknesses: strict formatting instructions, coding, and occasionally confident wrong facts. Check important answers.
License: Apache 2.0 (same as the base model).

Citation

@misc{miniai2026quata2preview,
  title        = {MiniAI Quata2 4B Preview},
  author       = {{MiniAI}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/M1n1A1/MiniAI-Quata2-4b-Preview}},
  note         = {Preview build, fine-tuned from Qwen3.5-4B}
}

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for M1n1A1/MiniAI-Quata2-4b-Preview

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(574)

this model