Instructions to use DFveloper/AIKAR-1.2-Pro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DFveloper/AIKAR-1.2-Pro-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="DFveloper/AIKAR-1.2-Pro-GGUF")
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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DFveloper/AIKAR-1.2-Pro-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf DFveloper/AIKAR-1.2-Pro-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DFveloper/AIKAR-1.2-Pro-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DFveloper/AIKAR-1.2-Pro-GGUF:BF16

Use Docker

docker model run hf.co/DFveloper/AIKAR-1.2-Pro-GGUF:BF16

LM Studio
Jan

vLLM

How to use DFveloper/AIKAR-1.2-Pro-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DFveloper/AIKAR-1.2-Pro-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DFveloper/AIKAR-1.2-Pro-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/DFveloper/AIKAR-1.2-Pro-GGUF:BF16

SGLang

How to use DFveloper/AIKAR-1.2-Pro-GGUF 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 "DFveloper/AIKAR-1.2-Pro-GGUF" \
    --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": "DFveloper/AIKAR-1.2-Pro-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "DFveloper/AIKAR-1.2-Pro-GGUF" \
        --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": "DFveloper/AIKAR-1.2-Pro-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use DFveloper/AIKAR-1.2-Pro-GGUF with Ollama:
```
ollama run hf.co/DFveloper/AIKAR-1.2-Pro-GGUF:BF16
```

Unsloth Studio

How to use DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DFveloper/AIKAR-1.2-Pro-GGUF to start chatting

How to use DFveloper/AIKAR-1.2-Pro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DFveloper/AIKAR-1.2-Pro-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": "DFveloper/AIKAR-1.2-Pro-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DFveloper/AIKAR-1.2-Pro-GGUF with Docker Model Runner:
```
docker model run hf.co/DFveloper/AIKAR-1.2-Pro-GGUF:BF16
```

Lemonade

How to use DFveloper/AIKAR-1.2-Pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DFveloper/AIKAR-1.2-Pro-GGUF:BF16

Run and chat with the model

lemonade run user.AIKAR-1.2-Pro-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use DFveloper/AIKAR-1.2-Pro-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DFveloper/AIKAR-1.2-Pro-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 DFveloper/AIKAR-1.2-Pro-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DFveloper/AIKAR-1.2-Pro-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DFveloper/AIKAR-1.2-Pro-GGUF:BF16

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 "DFveloper/AIKAR-1.2-Pro-GGUF:BF16" \
  --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"

[AIKAR 1.2 Pro - GGUF] 📦

🔍 Overview

AIKAR 1.2 Pro - GGUF는 고성능 LLM인 AIKAR 1.2 Pro를 일반 사용자 환경에서도 효율적으로 구동할 수 있도록 llama.cpp 포맷으로 양자화(Quantization)한 버전입니다.

이 리포지토리는 고사양 GPU가 없는 환경에서도 모델의 지능을 최대한 유지하면서, 메모리(RAM/VRAM) 사용량을 최적화하여 로컬 추론(Local Inference)이 가능하도록 설계되었습니다.

🚀 Quick Start (Local Execution)

1. Using `llama.cpp`

가장 직접적인 방법으로, 터미널에서 다음과 같이 실행할 수 있습니다.

# 빌드된 llama.cpp 경로에서 실행
./main -m AIKAR-1.2-Pro-G2Q3.gguf -p "Tell me a long story" -n 128 -t 8

-t 8: 사용할 CPU 코어(Thread) 수를 지정합니다.

2. Using `DFveloper/aikar-engine` from Github

AMD GPU에 최적화된 엔진으로, llama.cpp 기반입니다. LOOP Chat의 실사용 서빙 엔진입니다.

# 빌드된 aikar-engine 경로에서 실행
./main -m AIKAR-1.2-Pro-G2Q3.gguf -p "Tell me a long story" -n 128 -t 8

-t 8: 사용할 CPU 코어(Thread) 수를 지정합니다.

🛠 Technical Specifications

Base Model: AIKAR 1.2 Pro (Full Precision)
Quantization Method: AIKAR Quant
Architecture: Transformer-based Decoder-only
Inference Engine: Optimized for aikar-engine

🤝 Feedback & Contribution

GGUF 양자화& 실행 과정에서 발생하는 성능 저하나 버그는 LOOP GitHub의 Issue 탭에 제보해 주세요. 사용자의 피드백은 더 정교한 양자화 가중치를 만드는 데 큰 도움이 됩니다.

"Bring the power of AIKAR 1.2 Pro to your local machine." — Optimized by LOOP

Downloads last month: 22

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

16-bit

View +1 variant

Model tree for DFveloper/AIKAR-1.2-Pro-GGUF

Base model

google/gemma-4-26B-A4B

Finetuned

DFveloper/AIKAR-1.2-Pro

Quantized

(3)

this model

[AIKAR 1.2 Pro - GGUF] 📦

🔍 Overview

🚀 Quick Start (Local Execution)

1. Using llama.cpp

2. Using DFveloper/aikar-engine from Github

🛠 Technical Specifications

🤝 Feedback & Contribution

Model tree for DFveloper/AIKAR-1.2-Pro-GGUF

1. Using `llama.cpp`

2. Using `DFveloper/aikar-engine` from Github