Instructions to use M1n1A1/MiniAI-Quata2-4b-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="M1n1A1/MiniAI-Quata2-4b-v0.2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("M1n1A1/MiniAI-Quata2-4b-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("M1n1A1/MiniAI-Quata2-4b-v0.2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata2-4b-v0.2: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-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M

Use Docker

docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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-v0.2"
# 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-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M

SGLang

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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 "M1n1A1/MiniAI-Quata2-4b-v0.2" \
    --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": "M1n1A1/MiniAI-Quata2-4b-v0.2",
		"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 "M1n1A1/MiniAI-Quata2-4b-v0.2" \
        --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": "M1n1A1/MiniAI-Quata2-4b-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use M1n1A1/MiniAI-Quata2-4b-v0.2 with Ollama:
```
ollama run hf.co/M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M
```
Unsloth Desktop

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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-v0.2: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-v0.2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use M1n1A1/MiniAI-Quata2-4b-v0.2 with Docker Model Runner:
```
docker model run hf.co/M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M
```

Lemonade

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull M1n1A1/MiniAI-Quata2-4b-v0.2:Q4_K_M

Run and chat with the model

lemonade run user.MiniAI-Quata2-4b-v0.2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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-v0.2: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-v0.2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use M1n1A1/MiniAI-Quata2-4b-v0.2 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-v0.2: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-v0.2: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) — v0.2

A 4B that holds its own: matches its base on general benchmarks, wins on tools, speaks Serbian, thinks before it speaks.

Quata2 4B v0.2 is MiniAI's new compact model from Belgrade. It is built on Qwen3.5-4B with Serbian continued pretraining, a new fine-tune and verified reasoning traces, then blended 80/20 with its base to keep the base's general strength. It runs on one 8 GB GPU (~2.8 GB at Q4_K_M), and the whole thing was trained on one 8 GB GPU too.

Highlights

Even with its base on general benchmarks, ahead on tools: against Qwen3.5-4B on 22 public benchmarks, 2 real wins (Winogrande, BFCL tool calling), 1 real loss (DROP), 19 ties.
Tool calling (BFCL): 88.7% vs 85.2% for the base.
Serbian: ties YugoGPT 7B on its own Serbian LLM Eval (59.0 vs 60.5, within the margin) at about half the size, ahead of its base (57.9); Belebele Serbian 86.6% vs 84.6%.
Thinking mode: MATH-500 95.0%, MMLU-Pro 81.0%, GPQA Diamond 62.6%.
Against other 4B-class models (thinking off): vs Qwen3 4B 2507 8 real wins / 3 losses; vs Gemma 4 E4B 6 real wins / 3 losses.
Knows who it is with no system prompt, holds a correct answer under pushback, solves more real agent tasks than its base.

How it was made

Serbian continued pretraining: ~50M tokens of Serbian (Latin and Cyrillic) and English text → v0.2-base.
Fine-tune: ~31,000 examples (chat, agents, tool calls, code, instruction following, maths, Serbian, uncertainty, identity) plus verified step-by-step reasoning traces.
Blend: 80% fine-tuned weights + 20% Qwen3.5-4B (WiSE-FT). The raw fine-tune was behind its base on MMLU-Pro, GSM8K and instruction following; the blend wins those back while keeping the new skills.

Benchmarks (thinking off)

Same harness, prompts, settings and question samples for every model; Q4_K_M GGUF on llama.cpp, no system prompt. ✅ / ❌ = a real win / loss against the base (beyond 2 standard errors); unmarked rows are ties. Best per row in bold.

Benchmark	Quata2 4B v0.2	Qwen3.5 4B (base)	Qwen3 4B 2507	Gemma 4 E4B
MMLU	73.0	70.6	64.6	65.0
MMLU-Redux 2.0	75.4	75.4	72.4	68.8
MMLU-Pro	68.7	71.3	66.3	66.0
GPQA Diamond	39.9	37.4	29.3	40.4
SuperGPQA	35.7	33.0	29.7	32.3
ARC-Challenge	91.6	92.4	92.0	89.2
HellaSwag	86.0	86.0	84.6	80.6
Winogrande ✅	66.2	63.6	60.4	64.6
TruthfulQA (MC1)	71.0	73.0	67.0	66.2
BIG-Bench Hard	76.0	75.3	67.2	78.3
MuSR	52.1	49.6	37.1	61.7
DROP (F1) ❌	60.5	63.8	34.5	37.6
GSM8K	85.6	86.6	88.8	86.4
MATH-500	79.4	80.2	77.2	77.2
IFEval (strict)	78.8	82.3	83.6	84.7
BFCL tool calling ✅	88.7	85.2	91.7	89.9
HumanEval+	77.4	75.6	84.8	85.4
MBPP+	66.9	64.8	63.0	67.5
Belebele Serbian	86.6	84.6	84.2	86.8
Belebele Croatian	85.0	84.0	83.2	87.0
Global-MMLU Serbian	60.0	58.4	50.0	51.8
MMLU-ProX Serbian	55.0	58.3	52.0	56.3

Thinking mode

Thinking on, up to 32k tokens, sampling 0.6 / 0.95 / 20. Comparisons with other models in thinking mode are still running.

Benchmark	Thinking off	Thinking on	Finished within 32k
GSM8K	85.6	85.0	100%
MATH-500	79.4	95.0	97%
MMLU-Pro	68.7	81.0	98%
GPQA Diamond	39.9	62.6	88%
SuperGPQA	35.7	47.0	92%
BIG-Bench Hard	76.0	86.7	100%
AIME 2024 + 2025	—	51.7	72%

Serbian LLM Eval (YugoGPT's suite)

Log-likelihood, 7 tasks × 500 questions, same script and settings for every model (4-bit).

Model	Mean
Quata2 4B v0.2	59.0
YugoGPT 7B	60.5
Qwen3.5 4B (base)	57.9
Qwen3 4B 2507	51.0

Quata's own checks

Targeted tests with the website's system prompt. Small sets (12–300 items), so read them as directional.

Check	Quata2 4B v0.2	Qwen3.5 4B (base)	Items
Knows it is Quata2 (no system prompt)	100.0	0.0	300
Stays in a customer persona	100.0	97.0	100
Facts about Serbia	75.0	62.5	12
Knows where companies are	91.7	91.7	12
Doesn't put every company in Belgrade	90.9	100.0	12
Holds a correct answer under pushback	75.0	65.0	60
Fixes a real mistake when challenged	80.0	95.0	60
Real tool-using agent tasks, solved	85.4	80.1	151
Multi-step agent tasks, solved	78.4	74.5	151

Reading the numbers: what is noise

Every benchmark is a sample of questions, so every score has a margin of error. For two models on the same 500 questions, the gap needs to be about 2–4 points before it means anything; on the small targeted checks (20–40 questions) it needs to be 15–25 points. We call a difference real only when it is larger than twice its standard error, computed pair by pair on the same questions. Anything smaller is a tie, even if one number is higher: a 1-point gap on a 500-question test is about 5 questions, a coin flip rather than a result.

Get started

Run it with Transformers

from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration

model_id = "M1n1A1/MiniAI-Quata2-4b-v0.2"
tok = AutoTokenizer.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)

messages = [{"role": "user", "content": "Ko te je napravio?"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True,
                                 enable_thinking=False)   # True = think step by step first
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

The safetensors include the Qwen3.5 vision tower; the GGUF is text-only.

Thinking mode: enable_thinking=True, or put /think / /no_think in a message. Give it room when it thinks: our thinking-mode numbers use up to 32,000 new tokens.

Run it with llama.cpp

llama-server -m MiniAI-Quata2-4b-v0.2-Q4_K_M.gguf --jinja -c 8192

Recommended sampling: temperature 0.7, top_p 0.8, top_k 20 (thinking: 0.6, 0.95, 20).

Details

Architecture: Qwen3.5-4B, 4.2B language parameters (+0.33B vision, +0.12B MTP), 32 layers, hidden size 2560, hybrid attention
Weights: 0.8 × fine-tuned Quata2 + 0.2 × Qwen3.5-4B (WiSE-FT blend)
Context length: 262,144 tokens in the model config; training used up to 2,048-token sequences
Precision: bf16 safetensors (~~9.7 GB with MTP); Q4_K_M GGUF (~~2.8 GB)
Languages: English first; Serbian, Croatian and Bosnian improved, grammar still behind English
License: Apache 2.0 (same as the base model)

Honest caveats

Real losses against the base: DROP. The base is also slightly ahead on IFEval and MMLU-Pro (within noise).
Qwen3 4B 2507 and Gemma 4 E4B are ahead on coding (HumanEval+) and instruction following (IFEval); Qwen3 4B 2507 also on tool calling (BFCL), Gemma 4 E4B on MuSR, BIG-Bench Hard and Belebele.
Serbian is improved but not native-level; on YugoGPT's suite YugoGPT is stronger at sentence completion (HellaSwag, PIQA), Quata at reading and reasoning (BoolQ, ARC).
Thinking-mode comparisons with other models are still running. Check important answers.

Citation

@misc{miniai2026quata2v02,
  title        = {MiniAI Quata2 4B v0.2},
  author       = {{MiniAI}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/M1n1A1/MiniAI-Quata2-4b-v0.2}},
  note         = {Fine-tuned from and blended with Qwen3.5-4B}
}

Downloads last month: 34

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for M1n1A1/MiniAI-Quata2-4b-v0.2

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

M1n1A1/MiniAI-Quata2-4b-v0.2-base

Quantized

(4)

this model

Quantizations

2 models